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We focus on the disparity of CNAs in tumour samples, which were compared to those in blood in order to identify the directional loss of heterozygosity. We propose a numerical algorithm and apply it to data from the Illumina 109K-SNP array on 112 samples from breast cancer patients. B-allele frequency (BAF) and log R ratio (LRR) of Illumina were used to estimate Euclidian distances. For each locus, we compared genotypes in blood and tumour for subset of samples being heterozygous in blood. We identified loci showing preferential disparity from heterozygous toward either the A\u002FB-allele homozygous (allelic disparity). The chi-squared and Cochran-Armitage trend tests were used to examine whether there is an association between high levels of disparity in single nucleotide polymorphisms (SNPs) and molecular, clinical and tumour-related parameters. To identify pathways and network functions over-represented within the resulting gene sets, we used Ingenuity Pathway Analysis (IPA). To identify loci with a high level of disparity, we selected SNPs 1) with a substantial degree of disparity and 2) with substantial frequency (at least 50% of the samples heterozygous for the respective locus). We report the overall difference in disparity in high-grade tumours compared to low-grade tumours (p-value \u003C 0.001) and significant associations between disparity in multiple single loci and clinical parameters. The most significantly associated network functions within the genes represented in the loci of disparity were identified, including lipid metabolism, small-molecule biochemistry, and nervous system development and function. No evidence for over-representation of directional disparity in a list of stem cell genes was obtained, however genes appeared to be more often altered by deletion than by amplification. Our data suggest that directional loss and amplification exist in breast cancer. These are highly associated with grade, which may indicate that they are enforced with increasing number of cell divisions. 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Genes Chromosomes Cancer. 2008, 47: 680-696. 10.1002\u002Fgcc.20569.","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fgcc.20569",{"mag":408,"openalex":409,"pm":410,"doi":411},"2132558332","W2132558332","18398821","10.1002\u002Fgcc.20569",{"id":353,"text":413,"url":355,"identifiers":414},"Staaf J, Vallon-Christersson J, Lindgren D, Juliusson G, Rosenquist R, Hoglund M, Borg A, Ringner M: Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios. BMC Bioinformatics. 2008, 9: 409-10.1186\u002F1471-2105-9-409.",{"doi":357},{"id":18,"text":416,"url":417,"identifiers":418},"Peiffer DA, Le JM, Steemers FJ, Chang W, Jenniges T, Garcia F, Haden K, Li J, Shaw CA, Belmont J, Cheung SW, Shen RM, Barker DL, Gunderson KL: High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res. 2006, 16: 1136-1148. 10.1101\u002Fgr.5402306.","https:\u002F\u002Fdoi.org\u002F10.1101\u002Fgr.5402306",{"mag":419,"pmc":420,"openalex":421,"pm":422,"doi":423},"2146948473","1557768","W2146948473","16899659","10.1101\u002Fgr.5402306",{"id":18,"text":425,"url":426,"identifiers":427},"Genomic profiling of LOH and DNA copy number with Infinium® Whole-Genome Genotyping, technical note: DNA Analysis. [http:\u002F\u002Fwww.illumina.com\u002FDocuments\u002Fproducts\u002Fappnotes\u002Fappnote_cgh.pdf]","http:\u002F\u002Fwww.illumina.com\u002FDocuments\u002Fproducts\u002Fappnotes\u002Fappnote_cgh.pdf",{},{"id":18,"text":429,"url":430,"identifiers":431},"Infinium® Genotyping Data Analysis, technical note: Illumina® DNA Analysis. 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Science. 2007, 318: 1917-1920. 10.1126\u002Fscience.1151526.","https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1151526",{"mag":575,"openalex":576,"pm":577,"doi":578},"2142046859","W2142046859","18029452","10.1126\u002Fscience.1151526",{"id":18,"text":580,"url":581,"identifiers":582},"The pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1755-8794\u002F4\u002F85\u002Fprepub","http:\u002F\u002Fwww.biomedcentral.com\u002F1755-8794\u002F4\u002F85\u002Fprepub",{},false,{"id":585,"createTime":586,"updateTime":587,"relativeEntities":588,"slug":589,"properties":590,"entityType":171,"verifyStatus":172,"verifyTime":601,"verifyNote":174,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":602,"fullTextUrl":18,"authors":603,"publicationType":283,"publisherRelationship":668,"citationCount":19,"citationInfo":719,"publishDate":722,"publishYear":720,"citationAnalyzeStatus":339,"lastCitationAnalyze":587,"indexDatabases":723,"openAccess":18,"references":18,"isForceReanalyzing":583},"022d0b38-732a-4f0a-810e-61a8c555166e","2024-01-08T18:40:25.204+00:00","2026-08-18T19:18:33.196+00:00",[],"Soft-tissue-sarcoma-subtypes-exhibit-distinct-patterns-of-acquired-uniparental-disomy",{"abstract":591,"title":593,"gsPaper":595,"references":597,"doi":599},{"EN":592},"Soft tissue sarcomas (STS) are heterogeneous mesenchymal tumors with diverse subtypes. STS can be classified into two main categories according to the type of genomic alteration: recurrent translocation driven STS, and non-recurrent translocations. However, little has known about acquired uniparental disomy in STS. In this study, we analyzed SNP microarray data to determine the frequency and distribution patterns of acquired uniparental disomy (aUPD) in major soft tissue sarcoma (STS) subtypes using CNAG and R softwares. We identified recurrent aUPD regions specific to alveolar rhabdomyosarcoma with the most frequent at 11p15.4, gastrointestinal stromal tumor at 1p36.11-p35.3, leiomyosarcoma at 17p13.3-p13.1, myxofibrosarcoma at 1p35.1-p34.2 and 16q23.3-q24.1, and pleomorphic liposarcoma at 13q13.2-q13.3 and 13q14.11-q14.2. In contrast, specific recurrent aUPD regions were not identified in dedifferentiated liposarcoma, Ewing sarcoma, myxoid\u002Fround cell liposarcoma, and synovial sarcoma. Strikingly total, centromeric and segmental aUPD regions are more frequent in STS that do not exhibit recurrent translocation events. Our study yields a detailed map of aUPD across 9 diverse STS subtypes and suggests the potential location of several novel tumor suppressor genes and oncogenes.",{"EN":594},"Soft tissue sarcoma subtypes exhibit distinct patterns of acquired uniparental disomy",{"VOID":596},"[\"7525150940708685371\"]",{"VOID":598},"Chibon F, Lagarde P, Salas S, Perot G, Brouste V, Tirode F, Lucchesi C, de Reynies A, Kauffmann A, Bui B, et al: Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat Med. 2010, 16 (7): 781-787. 10.1038\u002Fnm.2174.\nRieker RJ, Weitz J, Lehner B, Egerer G, Mueller A, Kasper B, Schirmacher P, Joos S, Mechtersheimer G: Genomic profiling reveals subsets of dedifferentiated liposarcoma to follow separate molecular pathways. 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Science. 1998, 279 (5350): 577-580. 10.1126\u002Fscience.279.5350.577.\nGunawan B, von Heydebreck A, Sander B, Schulten HJ, Haller F, Langer C, Armbrust T, Bollmann M, Gasparov S, Kovac D, et al: An oncogenetic tree model in gastrointestinal stromal tumours (GISTs) identifies different pathways of cytogenetic evolution with prognostic implications. J Pathol. 2007, 211 (4): 463-470. 10.1002\u002Fpath.2128.\nUl-Hassan A, Sisley K, Hughes D, Hammond DW, Robinson MH, Reed MW: Common genetic changes in leiomyosarcoma and gastrointestinal stromal tumour: implication for ataxia telangiectasia mutated involvement. Int J Exp Pathol. 2009, 90 (5): 549-557. 10.1111\u002Fj.1365-2613.2009.00680.x.\nLittle M, Van Heyningen V, Hastie N: Dads and disomy and disease. Nature. 1991, 351 (6328): 609-610. 10.1038\u002F351609a0.\nAnderson J, Gordon A, McManus A, Shipley J, Pritchard-Jones K: Disruption of imprinted genes at chromosome region 11p15.5 in paediatric rhabdomyosarcoma. Neoplasia. 1999, 1 (4): 340-348. 10.1038\u002Fsj.neo.7900052.\nCasola S, Pedone PV, Cavazzana AO, Basso G, Luksch R, D'Amore ES, Carli M, Bruni CB, Riccio A: Expression and parental imprinting of the H19 gene in human rhabdomyosarcoma. Oncogene. 1997, 14 (12): 1503-1510. 10.1038\u002Fsj.onc.1200956.\nGallego Melcon S, Sanchez de Toledo Codina J: Molecular biology of rhabdomyosarcoma. Clin Transl Oncol. 2007, 9 (7): 415-419. 10.1007\u002Fs12094-007-0079-3.\nThe pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1755-8794\u002F5\u002F60\u002Fprepub",{"VOID":600},"10.1186\u002F1755-8794-5-60","2024-06-24T16:27:42.077+00:00","https:\u002F\u002Fbmcmedgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1755-8794-5-60",[604,619,634,651],{"id":605,"sortIndex":19,"researcher":18,"roles":606,"affiliations":607,"properties":616,"displayName":618,"givenName":18,"familyName":18},"b660d7bf-eb4f-4d31-95b3-f499aa8ff6ac",[180],[608],{"id":609,"sortIndex":19,"affiliation":610,"properties":18},"13c3d8bd-992a-4032-9400-78162f0c50ca",{"id":609,"createTime":18,"updateTime":18,"relativeEntities":611,"slug":18,"properties":612,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":615,"statistic":18},[],{"title":613},{"VI":614},"Department of Epidemiology, Unit 1340, The University of Texas MD Anderson Cancer Center, Houston, USA",[],{"title":617},{"VI":618},"Musaffe Tuna",{"id":620,"sortIndex":195,"researcher":18,"roles":621,"affiliations":622,"properties":631,"displayName":633,"givenName":18,"familyName":18},"f6a4f341-fe36-457f-bd21-87221ef031e9",[180],[623],{"id":624,"sortIndex":19,"affiliation":625,"properties":18},"9c59475d-bc81-4cc0-9b6b-08e0b55a5888",{"id":624,"createTime":18,"updateTime":18,"relativeEntities":626,"slug":18,"properties":627,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":630,"statistic":18},[],{"title":628},{"VI":629},"Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA",[],{"title":632},{"VI":633},"Zhenlin 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albinism (OCA) is an autosomal recessive disorder characterized by hypo-pigmentation of skin, hair, and eyes. The OCA clinical presentation is due to a deficiency of melanin biosynthesis. Intellectual disability (ID) in OCA cases is a rare clinical presentation and appropriate diagnosis of ID is challenging through clinical examination. We report an Indian family with a rare co-inheritance of OCA1B and ID due to a novel TYR gene variant and chromosomal copy number variations. We have done a study on three siblings (2 males and 1 female) of a family where all of them presented with hypopigmented skin, hair and eyes. The male children and their father was affected with ID. Targeted exome sequencing and multiplex ligation-dependent probe amplification analysis were carried out to identify the OCA1B and ID associated genomic changes. Further Array-CGH was performed using SurePrint G3 Human CGH + SNP, 8*60 K array. A rare homozygous deletion of exon 3 in TYR gene causing OCA1B was identified in all three children. The parents were found to be heterozygous carriers. The Array-CGH analysis revealed paternally inherited heterozygous deletion (1.9 MB) of 15q11.1-> 15q11.2 region in all three children. Additionally, paternally inherited heterozygous deletion (2.6 MB) of 10q23.2-> 10q23.31 region was identified in the first male child; this may be associated with ID as the father and the child both presented with ID. While the 2nd male child had a denovo duplication of 13q31.1-> 13q31.3 chromosomal region. A rare homozygous TYR gene exon 3 deletion in the present study is the cause of OCA1B in all three children, and the additional copy number variations are associated with the ID. The study highlights the importance of combinational genetic approaches for diagnosing two different co-inherited disorders (OCA and ID). Hence, OCA cases with additional clinical presentation need to be studied in-depth for the appropriate management of the disease.",{"EN":734},"Novel deletion of exon 3 in TYR gene causing Oculocutaneous albinism 1B in an Indian family along with intellectual disability associated with chromosomal copy number variations",{"VOID":736},"[\"6345062298492289509\"]",{"VOID":738},"Spritz RA, Chiang PW, Oiso N, Alkhateeb A. Human and mouse disorders of pigmentation. Curr Opin Genet Dev. 2003;13(3):284–9.\nMontoliu L, Grønskov K, Wei AH, Martínez-García M, Fernández A, Arveiler B, Morice-Picard F, Riazuddin S, Suzuki T, Ahmed ZM, Rosenberg T. Increasing the complexity: new genes and new types of albinism. Pigment Cell Melanoma Res. 2014;27(1):11–8.\nFederico JR, Krishnamurthy K. Albinism. In: StatPearls. StatPearls Publishing, Treasure Island (FL); 2020. PMID: 30085560.\nGrønskov K, Dooley CM, Østergaard E, Kelsh RN, Hansen L, Levesque MP, Vilhelmsen K, Møllgård K, Stemple DL, Rosenberg T. Mutations in c10orf11, a melanocyte-differentiation gene, cause autosomal-recessive albinism. Am J Hum Genet. 2013;92(3):415–21.\nWei AH, Zang DJ, Zhang Z, Liu XZ, He X, Yang L, Wang Y, Zhou ZY, Zhang MR, Dai LL, Yang XM. Exome sequencing identifies SLC24A5 as a candidate gene for nonsyndromic oculocutaneous albinism. J Investig Dermatol. 2013;133(7):1834–40.\nGrønskov K, Ek J, Brondum-Nielsen K. Oculocutaneous albinism. Orphanet J Rare Dis. 2007;2(1):1–8.\nMohamed AF, El-Sayed NS, Seifeldin NS. Clinico-epidemiologic features of oculocutaneous albinism in northeast section of Cairo-Egypt. Egypt J Med Hum Genet. 2010;11(2):167–72.\nCooksey CJ, Garratt PJ, Land EJ, Pavel S, Ramsden CA, Riley PA, Smit NP. Evidence of the indirect formation of the catecholic intermediate substrate responsible for the autoactivation kinetics of tyrosinase. J Biol Chem. 1997;272(42):26226–35.\nFryer JP, Oetting WS, Brott MJ, King RA. Alternative splicing of the tyrosinase gene transcript in normal human melanocytes and lymphocytes. J Investig Dermatol. 2001;117(5):1261–5.\nAigner B, Besenfelder U, Müller M, Brem G. Tyrosinase gene variants in different rabbit strains. Mamm Genome. 2000;11(8):700–2.\nScheinfeld NS. Syndromic albinism: a review of genetics and phenotypes. Dermatol Online J. 2003. https:\u002F\u002Fdoi.org\u002F10.5070\u002FD30FB7F671.\nBoissy RE. Dermatologic Manifestations of Albinism: Background, Pathophysiology, Etiology of Albinism, Epidemiology, Prognosis, Patient Education [Internet]. 2019 [cited 2021 May 13]. https:\u002F\u002Femedicine.medscape.com\u002Farticle\u002F1068184-overview\nChinagi DR, Patil LS, Giraddi T, Ugargol P. Scholars Journal of Medical Case Reports ISSN 2347-6559 (Online).\nArons B, Kosek JC, Forrest IS. Chlorpromazine therapy in a female albino mental patient: clinical, histochemical and biochemical observations. Life Sci. 1968;7(24):1273–80.\nRinchik EM, Bultman SJ, Horsthemke B, Lee ST, Strunk KM, Spritz RA, Avidano KM, Jong MT, Nicholls RD. A gene for the mouse pink-eyed dilution locus and for human type II oculocutaneous albinism. Nature. 1993;361(6407):72–6.\nLee ST, Nicholls RD, Bundey S, Laxova R, Musarella M, Spritz RA. Mutations of the P gene in oculocutaneous albinism, ocular albinism, and Prader-Willi syndrome plus albinism. N Engl J Med. 1994;330(8):529–34.\nRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–23. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fgim.2015.30.\nSun W, Shen Y, Shan S, Han L, Li Y, Zhou Z, Zhong Z, Chen J. Identification of TYRmutations in patients with oculocutaneous albinism. Mol Med Rep. 2018;17(6):8409–13.\nSchnur RE, Sellinger BT, Holmes SA, Wick PA, Tatsumura YO, Spritz RA. Type I oculocutaneous albinism associated with a full-length deletion of the tyrosinase gene. J Investig Dermatol. 1996;106(5):1137–40.\nShahzad M, Yousaf S, Waryah YM, Gul H, Kausar T, Tariq N, Mahmood U, Ali M, Khan MA, Waryah AM, Shaikh RS. Molecular outcomes, clinical consequences, and genetic diagnosis of Oculocutaneous Albinism in Pakistani population. Sci Rep. 2017;7(1):1–5.\nBlaszczyk WM, Distler C, Dekomien G, Arning L, Hoffmann KP, Epplen JT. Identification of a tyrosinase (TYR) exon 4 deletion in albino ferrets (Mustela putorius furo). Anim Genet. 2007;38(4):421–3.\nOpitz S, Käsmann-Kellner B, Kaufmann M, Schwinger E, Zühlke C. Detection of 53 novel DNA variations within the tyrosinase gene and accumulation of mutations in 17 patients with albinism. Hum Mutat. 2004;23(6):630–1.\nLiu Q, Collin RWJ, Cremers FPM, den Hollander AI, van den Born LI, et al. Expression of wild-type Rp1 protein in Rp1 knock-in mice rescues the retinal degeneration phenotype. PLoS ONE. 2012;7(8):e43251.\nFahim AT, Daiger SP, Weleber RG. Nonsyndromic retinitis pigmentosa overview. GeneReviews®[Internet]. 2017\nAgarwal RP, Parks RE Jr. Erythrocytic nucleoside diphosphokinase: V. Some properties and behavior of the pi 7.3 isozyme. J Biol Chem. 1971;246(7):2258–64.\nMiller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, Church DM, Crolla JA, Eichler EE, Epstein CJ, Faucett WA. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet. 2010;86(5):749–64.\nLee SH, Ryoo E, Tchah H. Bannayan–Riley–Ruvalcaba syndrome in a patient with a PTEN mutation identified by chromosomal microarray analysis: a case report. Pediatr Gastroenterol Hepatol Nutr. 2017;20(1):65–70.\nBennett KL, Mester J, Eng C. Germline epigenetic regulation of KILLIN in Cowden and Cowden-like syndrome. JAMA. 2010;304(24):2724–31.\nWaite KA, Eng C. Protean PTEN: form and function. Am J Hum Genet. 2002;70(4):829–44.\nVarga EA, Pastore M, Prior T, Herman GE, McBride KL. The prevalence of PTEN mutations in a clinical pediatric cohort with autism spectrum disorders, developmental delay, and macrocephaly. Genet Med. 2009;11(2):111–7.\nQuelin C, Spaggiari E, Khung-Savatovsky S, Dupont C, Pasquier L, Loeuillet L, Jaillard S, Lucas J, Marcorelles P, Journel H, Pluquailec-Bilavarn K. Inversion duplication deletions involving the long arm of chromosome 13: phenotypic description of additional three fetuses and genotype–phenotype correlation. Am J Med Genet A. 2014;164(10):2504–9.\nMenten B, Maas N, Thienpont B, Buysse K, Vandesompele J, Melotte C, de Ravel T, Van Vooren S, Balikova I, Backx L, Janssens S. Emerging patterns of cryptic chromosomal imbalance in patients with idiopathic mental retardation and multiple congenital anomalies: a new series of 140 patients and review of published reports. J Med Genet. 2006;43(8):625–33.",{"VOID":740},"10.1186\u002Fs12920-021-01152-1","2024-05-13T11:16:28.686+00:00","https:\u002F\u002Fbmcmedgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12920-021-01152-1",[744,759,772,785,800,813],{"id":745,"sortIndex":19,"researcher":18,"roles":746,"affiliations":747,"properties":756,"displayName":758,"givenName":18,"familyName":18},"dcb2232a-9fb9-4aca-93ed-157fd6a2f9c9",[180],[748],{"id":749,"sortIndex":19,"affiliation":750,"properties":18},"2911e34e-5d6d-414a-9f50-6523e6485753",{"id":749,"createTime":18,"updateTime":18,"relativeEntities":751,"slug":18,"properties":752,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":755,"statistic":18},[],{"title":753},{"VI":754},"Department of Cytogenetics, National Institute of Immunohaematology (ICMR), Mumbai, India",[],{"title":757},{"VI":758},"Somprakash 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drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at \n                  http:\u002F\u002F1.complexdrug.sinaapp.com\u002FDrug_Complex_Disease\u002FData_Download.html\n                  \n                ",{"EN":894},"Inferring drug-disease associations based on known protein complexes",{"VOID":896},"[\"1562689040247167694\"]",{"VOID":898},"Goh Kwang-Il, Cusick Michael, Valle David, Childs Barton, Vidal Marc, Barabási Albert-László: The human disease network. Proceedings of the National Academy of Sciences. 2007, 104: 8685-8690. 10.1073\u002Fpnas.0701361104.\nDiMasi Joseph: New drug development in the United States from 1963 to 1999. Clinical pharmacology and therapeutics. 2001, 69: 286-296. 10.1067\u002Fmcp.2001.115132.\nAdams Christopher, Van Brantner V: Estimating the cost of new drug development: is it really $802 million?. Health Affairs. 2006, 25: 420-428. 10.1377\u002Fhlthaff.25.2.420.\nvon Eichborn Joachim, Murgueitio Manuela, Dunkel Mathias, Koerner Soeren, Bourne Philip, Preissner Robert: PROMISCUOUS: a database for network-based drug-repositioning. Nucleic acids research. 2011, 39: D1060-D1066. 10.1093\u002Fnar\u002Fgkq1037.\nWu Zikai, Wang Yong, Chen Luonan: Network-based drug repositioning. Molecular BioSystems. 2013, 9: 1268-1281. 10.1039\u002Fc3mb25382a.\nRe Matteo, Valentini Giorgio: Network-based drug ranking and repositioning with respect to DrugBank therapeutic categories. IEEE\u002FACM Transactions on Computational Biology and Bioinformatics (TCBB). 2013, 10: 1359-1371.\nCheng Feixiong, Li Weihua, Zhou Yadi, Li Jie, Shen Jie, Lee Philip, et al: Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs). Molecular BioSystems. 2013, 9: 1316-1325. 10.1039\u002Fc3mb25309k.\nLee Hee, Bae Taejeong, Lee Ji-Hyun, Kim Dae, Oh Young, Jang Yeongjun, et al: Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug. BMC systems biology. 2012, 6: 80-10.1186\u002F1752-0509-6-80.\nSuthram Silpa, Dudley Joel, Chiang Annie, Chen Rong, Hastie Trevor, Butte Atul: Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS computational biology. 2010, 6: e1000662-10.1371\u002Fjournal.pcbi.1000662.\nSchadt Eric, Friend Stephen, Shaywitz David: A network view of disease and compound screening. Nature reviews Drug discovery. 2009, 8: 286-295. 10.1038\u002Fnrd2826.\nSegal Eran, Friedman Nir, Koller Daphne, Regev Aviv: A module map showing conditional activity of expression modules in cancer. 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Chinese Science Bulletin. 2012, 57: 2106-2112. 10.1007\u002Fs11434-012-4982-9.\nZhao Shiwen, Li Shao: A Co-module Approach for Elucidating Drug-Disease Associations and Revealing Their Molecular Basis. Bioinformatics. 2012, 28 (7): 955-961. 10.1093\u002Fbioinformatics\u002Fbts057.\nRuepp Andreas, Waegele Brigitte, Lechner Martin, Brauner Barbara, Dunger-Kaltenbach Irmtraud, Fobo Gisela, et al: CORUM: the comprehensive resource of mammalian protein complexes--2009. Nucleic acids research. 2009, gkp914-\nMattingly CJ, Rosenstein MC, Colby GT, Forrest JN, Boyer JL: The Comparative Toxicogenomics Database (CTD): a resource for comparative toxicological studies. Journal of Experimental Zoology Part A: Comparative Experimental Biology. 2006, 305: 689-692.\nWishart David, Knox Craig, Guo Chi An, Shrivastava Savita, Hassanali Murtaza, Stothard Paul, et al: DrugBank: a comprehensive resource for in silico drug discovery and exploration. 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Bioinformatics. 2009, 25: i63-i68. 10.1093\u002Fbioinformatics\u002Fbtp193.\nLiu Zhi-Ping, Wang Yong, Zhang Xiang-Sun, Xia Weiming, Chen Luonan: Detecting and analyzing differentially activated pathways in brain regions of Alzheimer's disease patients. Molecular BioSystems. 2011, 7: 1441-1452. 10.1039\u002Fc0mb00325e.\nDavis Peter Allan, Wiegers Thomas, Roberts Phoebe, King Benjamin, Lay Jean, Lennon-Hopkins Kelley, et al: A CTD-Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database. 2013, 2013: bat080-10.1093\u002Fdatabase\u002Fbat080.\nHuang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols. 2008, 4: 44-57. 10.1038\u002Fnprot.2008.211.\nHuang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research. 2009, 37: 1-13. 10.1093\u002Fnar\u002Fgkn923.\nBenjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B. 1995, 57: 289-300.\nNepusz Tamás, Yu Haiyuan, Paccanaro Alberto: Detecting overlapping protein complexes in protein-protein interaction networks. Nature methods. 2012, 9: 471-472. 10.1038\u002Fnmeth.1938.\nSmoot Michael, Ono Keiichiro, Ruscheinski Johannes, Wang Peng-Liang, Ideker Trey: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27: 431-432. 10.1093\u002Fbioinformatics\u002Fbtq675.\nStein Dan: In Review What Is a Mental Disorder? A Perspective From Cognitive-Affective Science. Canadian Journal of Psychiatry. 2013, 58:\nKhan Arif, Cutler Andrew, Kajdasz Daniel, Gallipoli Susan, Athanasiou Maria, Robinson Donald, et al: A randomized, double-blind, placebo-controlled, 8-week study of vilazodone, a serotonergic agent for the treatment of major depressive disorder. The Journal of clinical psychiatry. 2011, 72: 441-447. 10.4088\u002FJCP.10m06596.\nBechelli LP, Ruffino-Netto A, Hetem G: A double-blind controlled trial of pipotiazine, haloperidol and placebo in recently-hospitalized acute schizophrenic patients. Brazilian journal of medical and biological research=Revista brasileira de pesquisas medicas e biologicas\u002FSociedade Brasileira de Biofisica...[et al.]. 1983, 16: 305-311.\nFinkel Richard, Clark Alexia Michelle, Cubeddu Luigi: Pharmacology. 2009, Philadelphia: Lippincott Williams & Wilkins, 4\nDorland's medical dictionary. January 30, 2008\nLanni Cristina, Lenzken Silvia, Pascale Alessia, Del Vecchio Igor, Racchi Marco, Pistoia Francesca, et al: Cognition enhancers between treating and doping the mind. Pharmacological Research. 2008, 57: 196-213. 10.1016\u002Fj.phrs.2008.02.004.\nPerez-Lloret Santiago, Rey Veronica Maria, Ratti Lucca Pietro, Rascol Olivier: Rotigotine transdermal patch for the treatment of Parkinson's Disease. Fundamental & clinical pharmacology. 2013, 27: 81-95. 10.1111\u002Fj.1472-8206.2012.01028.x.\nRomero Alarcon-de-la-Lastra, Lopez A, Martin MJ, La Casa C, Motilva V: Cinitapride Protects against Ethanol-lnduced Gastric Mucosal Injury in Rats: Role of 5-Hydroxytryptamine, Prostaglandins and Sulfhydryl Compounds. Pharmacology. 1997, 54: 193-202. 10.1159\u002F000139487.\nNichols David, Nichols Charles: Serotonin receptors. Chemical reviews. 2008, 108: 1614-1641. 10.1021\u002Fcr078224o.\nWay WL, Fields HL, Way EL, Katzung BG: Basic and clinical pharmacology. Basic and clinical pharmacology. 1998\nMard-Soltani Maysam, Kesmati Mahnnaz, Khajehpour Lotfolah, Rasekh Abdolrahman, Shamshirgar-Zadeh Abdolhosein: Interaction between Anxiolytic Effects of Testosterone and β-1 Adrenoceptors of Basolateral Amygdala. International Journal of Pharmacology. 2012, 8:\nElenkov Ilia, Wilder Ronald, Chrousos George, Vizi Sylvester: The sympathetic nerve--an integrative interface between two supersystems: the brain and the immune system. Pharmacological reviews. 2000, 52: 595-638.\nGutman Arie, Zaltsman Igor, Shalimov Anton, Sotrihin Maxim, Nisnevich Gennady, Yudovich Lev, et al: Process for the preparation of dexmethylphenidate hydrochloride. 2007, ed: Google Patents\nFuture Treatments for Depression, Anxiety, Sleep Disorders, Psychosis, and ADHD -- Neurotransmitter.net.\nPierre Fabre Medicament and Forest Laboratories to Collaborate on Development and Commercialization of F2695 for Depression - FierceBiotech.\nNews: Forest Buys CNS Disease-Related Drug for $75M Upfront.\nMaglione Margaret, Miotto Karen, Iguchi Martin, Jungvig Lara, Morton Sally, Shekelle Paul: Psychiatric effects of ephedra use: an analysis of Food and Drug Administration reports of adverse events. American Journal of Psychiatry. 2005, 162: 189-191. 10.1176\u002Fappi.ajp.162.1.189.\nAdkins DE, Khachane AN, McClay JL, Aberg K, Bukszar J, et al: SNPbased analysis of neuroactive ligand-receptor interaction pathways implicates PGE2 as a novel mediator of antipsychotic treatment response: Data from the CATIE study. Schizophrenia Research. 2012, 135: 200-201. 10.1016\u002Fj.schres.2011.11.002.\nHypertension. (2014, May 20) In Wikipedia, the free encyclopedia. Retrieved May 20, 2014, [http:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHypertension]\nReview: could Trimipramine maleate cause Essential hypertension?. Jun, 18, 2014, [http:\u002F\u002Fwww.ehealthme.com\u002Fds\u002Ftrimipramine+maleate\u002Fessential+hypertension]\nDrugs.com [Internet]. Paliperidone Information from Drugs.com; c2000-2014 [updated June 16th, 2014; Cited: 2014 June 18]. [http:\u002F\u002Fwww.drugs.com\u002Fsfx\u002Fpaliperidone-side-effects.html]\nBredberg U, Eyjolfsdottir GS, Paalzow L, Tfelt-Hansen P, Tfelt-Hansen V: Pharmacokinetics of methysergide and its metabolite methylergometrine in man. European journal of clinical pharmacology. 1986, 30: 75-77. 10.1007\u002FBF00614199.\nJasek W: Austria-Codex (in German) (62nded.). 2007, Vienna: Österreichischer Apothekerverlag, 5193-5.\nMontes Barbara Amy, Rey Jose: Iloperidone (Fanapt): An FDA-Approved Treatment Option for Schizophrenia. Pharmacy and Therapeutics. 2009, 34: 606-\nJeffrey Berman, Setty Arathi, Steiner Matthew, Kaufman Kenneth, Skotzko Christine: Complicated hypertension related to the abuse of ephedrine and caffeine alkaloids. Journal of addictive diseases. 2006, 25: 45-48. 10.1300\u002FJ069v25n03_06.\nde Toledo Ferraz Alves TC, Guerra de Andrade A: Hypertension induced by regular doses of milnacipran: a case report. Pharmacopsychiatry. 2007, 40: 41-42.\nMunoli Neelakanthappa Ravindra, Selvaraj Arun, Praharaj Kumar Samir, Bhandary Rajeshkrishna: Desvenlafaxine-Induced Worsening of Hypertension. The Journal of neuropsychiatry and clinical neurosciences,. 2013, 25: E29-E30.\nRummery NM, Hill CE: Vascular gap junctions and implications for hypertension. Clin Exp Pharmacol Physiol. 2004, 31: 659-667. 10.1111\u002Fj.1440-1681.2004.04071.x.\nvon Eichborn J, Murgueitio MS, Dunkel M, et al: PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res. 2011, 39: D1060-6. 10.1093\u002Fnar\u002Fgkq1037.\n[http:\u002F\u002Ftreato.com\u002FZolmitriptan,High+Blood+Pressure\u002F?a=s;http\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FZolmitriptan]\nDerby Michael, Zhang Lu, Chappell Jill, Gonzales Celedon, Callaghan JT, Leibowitz Mark, et al: The effects of supratherapeutic doses of duloxetine on blood pressure and pulse rate. Journal of cardiovascular pharmacology. 2007, 49: 384-393. 10.1097\u002FFJC.0b013e31804d1cce.\n[http:\u002F\u002Fwww.ehealthme.com\u002Fds\u002Ftapentadol+hydrochloride\u002Fhypertension]\n[http:\u002F\u002Fwww.ehealthme.com\u002Fds\u002Fviibryd\u002Fhypertension]\nMago Mahajan, Thase ME: Levomilnacipran: a newly approved drug for treatment of major depressive disorder. Expert Rev Clin Pharmacol. 2014, 7 (2): 137-45. 10.1586\u002F17512433.2014.889563. 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               \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Method\u003C\u002Fjats:title>\n                \u003Cjats:p>We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1–10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. 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The burden of respiratory syncytial virus infection in young children. N Engl J Med. 2009;360(6):588–98.",{"doi":1343},"10.1056\u002FNEJMoa0804877",{"id":18,"text":1345,"url":18,"identifiers":1346},"Shi T, McAllister DA, O’Brien KL, Simoes EAF, Madhi SA, Gessner BD, Polack FP, Balsells E, Acacio S, Aguayo C, et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. Lancet. 2017;390(10098):946–58.",{"doi":1347},"10.1016\u002FS0140-6736(17)30938-8",{"id":18,"text":1349,"url":18,"identifiers":1350},"Hall CB, Weinberg GA, Blumkin AK, Edwards KM, Staat MA, Schultz AF, Poehling KA, Szilagyi PG, Griffin MR, Williams JV, et al. Respiratory syncytial virus-associated hospitalizations among children less than 24 months of age. Pediatrics. 2013;132(2):e341-348.",{"doi":1351},"10.1542\u002Fpeds.2013-0303",{"id":18,"text":1353,"url":18,"identifiers":1354},"Bekhof J, Reimink R, Brand PL. Systematic review: insufficient validation of clinical scores for the assessment of acute dyspnoea in wheezing children. Paediatr Respir Rev. 2014;15(1):98–112.",{},{"id":18,"text":1356,"url":18,"identifiers":1357},"Corneli HM, Zorc JJ, Holubkov R, Bregstein JS, Brown KM, Mahajan P, Kuppermann N. Bronchiolitis Study Group for the Pediatric Emergency Care Applied Research N: Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99–103.",{"doi":1358},"10.1097\u002FPEC.0b013e3182440b9b",{"id":18,"text":1360,"url":18,"identifiers":1361},"Destino L, Weisgerber MC, Soung P, Bakalarski D, Yan K, Rehborg R, Wagner DR, Gorelick MH, Simpson P. Validity of respiratory scores in bronchiolitis. 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Association of dynamic changes in the CD4 T-cell transcriptome with disease severity during primary respiratory syncytial virus infection in young infants. J Infect Dis. 2017;216(8):1027–37.",{"doi":1409},"10.1093\u002Finfdis\u002Fjix400",{"id":18,"text":1411,"url":18,"identifiers":1412},"Mejias A, Dimo B, Suarez NM, Garcia C, Suarez-Arrabal MC, Jartti T, Blankenship D, Jordan-Villegas A, Ardura MI, Xu Z, et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 2013;10(11):e1001549.",{"doi":1413},"10.1371\u002Fjournal.pmed.1001549",{"id":18,"text":1415,"url":18,"identifiers":1416},"Do LAH, Pellet J, van Doorn HR, Tran AT, Nguyen BH, Tran TTL, Tran QH, Vo QB, Tran Dac NA, Trinh HN, et al. Host transcription profile in nasal epithelium and whole blood of hospitalized children under 2 years of age with respiratory syncytial virus infection. J Infect Dis. 2017;217(1):134–46.",{"doi":1417},"10.1093\u002Finfdis\u002Fjix519",{"id":18,"text":1419,"url":18,"identifiers":1420},"Walsh EE, Mariani TJ, Chu C, Grier A, Gill SR, Qiu X, Wang L, Holden-Wiltse J, Corbett A, Thakar J, et al. Aims, study design, and enrollment results from the assessing predictors of infant respiratory syncytial virus effects and severity study. JMIR Res Protoc. 2019;8(6):e12907.",{"doi":1421},"10.2196\u002F12907",{"id":18,"text":1423,"url":18,"identifiers":1424},"Chu CY, Qiu X, Wang L, Bhattacharya S, Lofthus G, Corbett A, Holden-Wiltse J, Grier A, Tesini B, Gill SR, et al. The healthy infant nasal transcriptome: a benchmark study. Sci Rep. 2016;6:33994.",{"doi":1425},"10.1038\u002Fsrep33994",{"id":18,"text":1427,"url":18,"identifiers":1428},"Caserta MT, Qiu X, Tesini B, Wang L, Murphy A, Corbett A, Topham DJ, Falsey AR, Holden-Wiltse J, Walsh EE. Development of a global respiratory severity score for respiratory syncytial virus infection in infants. J Infect Dis. 2017;215(5):750–6.",{},{"id":18,"text":1430,"url":18,"identifiers":1431},"Cheng L, Lo LY, Tang NL, Wang D, Leung KS. CrossNorm: a novel normalization strategy for microarray data in cancers. Sci Rep. 2016;6:18898.",{"doi":1432},"10.1038\u002Fsrep18898",{"id":18,"text":1434,"url":18,"identifiers":1435},"Cheng L, Wang X, Wong PK, Lee KY, Li L, Xu B, Wang D, Leung KS. ICN: a normalization method for gene expression data considering the over-expression of informative genes. Mol Biosyst. 2016;12(10):3057–66.",{"doi":1436},"10.1039\u002FC6MB00386A",{"id":18,"text":1438,"url":18,"identifiers":1439},"Liu X, Li N, Liu S, Wang J, Zhang N, Zheng X, Leung KS, Cheng L. Normalization methods for the analysis of unbalanced transcriptome data: a review. Front Bioeng Biotechnol. 2019;7:358.",{"doi":1440},"10.3389\u002Ffbioe.2019.00358",{"id":18,"text":1442,"url":18,"identifiers":1443},"Liu X, Zheng X, Wang J, Zhang N, Leung KS, Ye X, Cheng L. 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J Infect Dis (in press).",{},{"id":18,"text":1479,"url":18,"identifiers":1480},"Seber GA, Lee AJ. Linear regression analysis, vol. 329. Hoboken: Wiley; 2012.",{},{"id":18,"text":1482,"url":18,"identifiers":1483},"Opsomer J, Wang Y, Yang Y. Nonparametric regression with correlated errors. Stat Sci. 2001:134–153",{"doi":1484},"10.1214\u002Fss\u002F1009213287",{"id":18,"text":1486,"url":18,"identifiers":1487},"Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistics surveys. 2010;4:40–79.",{"doi":1488},"10.1214\u002F09-SS054",{"id":18,"text":1490,"url":18,"identifiers":1491},"Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27.",{"doi":1492},"10.1093\u002Fbiostatistics\u002Fkxj037",{"id":18,"text":1494,"url":18,"identifiers":1495},"Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724–35.",{"doi":1496},"10.1371\u002Fjournal.pgen.0030161",{"id":18,"text":1498,"url":18,"identifiers":1499},"Rudy J, Valafar F. Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinform. 2011;12:467.",{"doi":1500},"10.1186\u002F1471-2105-12-467",{"id":18,"text":1502,"url":18,"identifiers":1503},"Qiu X, Hu R, Wu Z. Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies. PLoS ONE. 2014;9(6):e99380.",{"doi":1504},"10.1371\u002Fjournal.pone.0099380",{"id":18,"text":1506,"url":18,"identifiers":1507},"Lee SH, Ruan SY, Pan SC, Lee TF, Chien JY, Hsueh PR. Performance of a multiplex PCR pneumonia panel for the identification of respiratory pathogens and the main determinants of resistance from the lower respiratory tract specimens of adult patients in intensive care units. J Microbiol Immunol Infect. 2019;52(6):920–8.",{"doi":1508},"10.1016\u002Fj.jmii.2019.10.009",{"id":1510,"createTime":1511,"updateTime":1512,"relativeEntities":1513,"slug":1514,"properties":1515,"entityType":171,"verifyStatus":172,"verifyTime":1526,"verifyNote":174,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1527,"fullTextUrl":18,"authors":1528,"publicationType":283,"publisherRelationship":1657,"citationCount":19,"citationInfo":1707,"publishDate":1039,"publishYear":1037,"citationAnalyzeStatus":17,"lastCitationAnalyze":1709,"indexDatabases":1710,"openAccess":18,"references":18,"isForceReanalyzing":583},"8549b5d8-4438-4bc9-80c6-b8c614d2dc42","2023-11-30T04:51:27.506+00:00","2026-07-23T12:51:04.874+00:00",[],"Novel-therapeutics-for-coronary-artery-disease-from-genome-wide-association-study-data",{"abstract":1516,"title":1518,"gsPaper":1520,"references":1522,"doi":1524},{"EN":1517},"Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (\n                  http:\u002F\u002Fwww.gentrepid.org\n                  \n                ), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p \u003C 0.05). We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.",{"EN":1519},"Novel therapeutics for coronary artery disease from genome-wide association study data",{"VOID":1521},"[\"12670848534241223363\"]",{"VOID":1523},"Mathers C, Fat DM, Boerma J: The global burden of disease: 2004 update. World Health Organization. 2008\nCohen B, Hasselbring B: Coronary Heart Disease: A guide to diagnosis and treatment. 2007, Addicus Books\nSwerdlow DI, Holmes MV, Harrison S, Humphries SE: The genetics of coronary heart disease. 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J Cheminform. 2013, 5: 30-10.1186\u002F1758-2946-5-30.",{"VOID":1525},"10.1186\u002F1755-8794-8-S2-S1","2024-05-12T02:15:18.019+00:00","https:\u002F\u002Fbmcmedgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1755-8794-8-S2-S1",[1529,1544,1564,1577,1592,1607,1629,1644],{"id":1530,"sortIndex":19,"researcher":18,"roles":1531,"affiliations":1532,"properties":1541,"displayName":1543,"givenName":18,"familyName":18},"ba7450d0-18fd-43c5-9dca-72b6cf5ff85c",[180],[1533],{"id":1534,"sortIndex":19,"affiliation":1535,"properties":18},"3bf7b6fd-84a6-4432-8a88-f1acba3df2ff",{"id":1534,"createTime":18,"updateTime":18,"relativeEntities":1536,"slug":18,"properties":1537,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1540,"statistic":18},[],{"title":1538},{"VI":1539},"School of Medicine, Deakin University, Geelong, Australia",[],{"title":1542},{"VI":1543},"Mani P Grover",{"id":1545,"sortIndex":195,"researcher":18,"roles":1546,"affiliations":1547,"properties":1559,"displayName":1561,"givenName":18,"familyName":18},"6cc1d5eb-7185-43de-a90e-632a3fb3575d",[180],[1548],{"id":1549,"sortIndex":19,"affiliation":1550,"properties":1556},"873d4438-6eaf-46b2-80c4-1b22d223912b",{"id":1549,"createTime":18,"updateTime":18,"relativeEntities":1551,"slug":18,"properties":1552,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1555,"statistic":18},[],{"title":1553},{"EN":1554},"Cold Spring Harbor Laboratory, Cold Spring Harbor, United States",[],{"title":1557},{"VI":1558},"Cold Spring Harbor Laboratory, Cold Spring Harbor, USA",{"title":1560,"gsAuthor":1562},{"VI":1561},"Sara 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Mohanasundaram",{"id":1578,"sortIndex":232,"researcher":18,"roles":1579,"affiliations":1580,"properties":1589,"displayName":1591,"givenName":18,"familyName":18},"8944be83-c537-4e19-8269-fe56db1d800c",[180],[1581],{"id":1582,"sortIndex":19,"affiliation":1583,"properties":18},"4a74192c-66aa-46ed-9af8-9860232f1c79",{"id":1582,"createTime":18,"updateTime":18,"relativeEntities":1584,"slug":18,"properties":1585,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1588,"statistic":18},[],{"title":1586},{"VI":1587},"Victor Chang Cardiac Research Institute, Darlinghurst, Australia",[],{"title":1590},{"VI":1591},"Richard A George",{"id":1593,"sortIndex":262,"researcher":18,"roles":1594,"affiliations":1595,"properties":1604,"displayName":1606,"givenName":18,"familyName":18},"e5233419-feb7-4be6-9f71-3574bed773e2",[180],[1596],{"id":1597,"sortIndex":19,"affiliation":1598,"properties":18},"3e11efea-a9e1-49bf-b22a-84ac5726591a",{"id":1597,"createTime":18,"updateTime":18,"relativeEntities":1599,"slug":18,"properties":1600,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1603,"statistic":18},[],{"title":1601},{"VI":1602},"School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, Australia",[],{"title":1605},{"VI":1606},"Andrzej Goscinski",{"id":1608,"sortIndex":815,"researcher":18,"roles":1609,"affiliations":1610,"properties":1626,"displayName":1628,"givenName":18,"familyName":18},"24a77956-d8df-4d80-8d70-b74b08e7c64b",[180],[1611,1617],{"id":1534,"sortIndex":19,"affiliation":1612,"properties":18},{"id":1534,"createTime":18,"updateTime":18,"relativeEntities":1613,"slug":18,"properties":1614,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1616,"statistic":18},[],{"title":1615},{"VI":1539},[],{"id":1618,"sortIndex":195,"affiliation":1619,"properties":1625},"9db4b9ee-c65c-482f-ab85-f87ca0b62f72",{"id":1618,"createTime":18,"updateTime":18,"relativeEntities":1620,"slug":18,"properties":1621,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1624,"statistic":18},[],{"title":1622},{"VI":1623},"Australian Animal Health Laboratory, CSIRO Biosecurity Flagship, Geelong, Australia",[],{},{"title":1627},{"VI":1628},"Tamsyn M Crowley",{"id":1630,"sortIndex":1176,"researcher":18,"roles":1631,"affiliations":1632,"properties":1641,"displayName":1643,"givenName":18,"familyName":18},"95196e32-784d-4149-a4fd-851d75e59f64",[180],[1633],{"id":1634,"sortIndex":19,"affiliation":1635,"properties":18},"d445286e-6e46-4dd7-a37d-e9de3a271787",{"id":1634,"createTime":18,"updateTime":18,"relativeEntities":1636,"slug":18,"properties":1637,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1640,"statistic":18},[],{"title":1638},{"VI":1639},"Life and Environmental Sciences, Deakin University, Geelong, Australia",[],{"title":1642},{"VI":1643},"Craig D H Sherman",{"id":1645,"sortIndex":1196,"researcher":18,"roles":1646,"affiliations":1647,"properties":1654,"displayName":1656,"givenName":18,"familyName":18},"33d915e9-35c7-4a64-ab59-8aa02b092200",[180],[1648],{"id":1534,"sortIndex":19,"affiliation":1649,"properties":18},{"id":1534,"createTime":18,"updateTime":18,"relativeEntities":1650,"slug":18,"properties":1651,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1653,"statistic":18},[],{"title":1652},{"VI":1539},[],{"title":1655},{"VI":1656},"Merridee A 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laboratories are adopting array genomic hybridization as a standard clinical test. A number of whole genome array genomic hybridization platforms are available, but little is known about their comparative performance in a clinical context. We studied 30 children with idiopathic MR and both unaffected parents of each child using Affymetrix 500 K GeneChip SNP arrays, Agilent Human Genome 244 K oligonucleotide arrays and NimbleGen 385 K Whole-Genome oligonucleotide arrays. We also determined whether CNVs called on these platforms were detected by Illumina Hap550 beadchips or SMRT 32 K BAC whole genome tiling arrays and tested 15 of the 30 trios on Affymetrix 6.0 SNP arrays. The Affymetrix 500 K, Agilent and NimbleGen platforms identified 3061 autosomal and 117 X chromosomal CNVs in the 30 trios. 147 of these CNVs appeared to be de novo, but only 34 (22%) were found on more than one platform. Performing genotype-phenotype correlations, we identified 7 most likely pathogenic and 2 possibly pathogenic CNVs for MR. All 9 of these putatively pathogenic CNVs were detected by the Affymetrix 500 K, Agilent, NimbleGen and the Illumina arrays, and 5 were found by the SMRT BAC array. Both putatively pathogenic CNVs identified in the 15 trios tested with the Affymetrix 6.0 were identified by this platform. Our findings demonstrate that different results are obtained with different platforms and illustrate the trade-off that exists between sensitivity and specificity. The large number of apparently false positive CNV calls on each of the platforms supports the need for validating clinically important findings with a different technology.",{"EN":1721},"Comparison of genome-wide array genomic hybridization platforms for the detection of copy number variants in idiopathic mental retardation",{"VOID":1723},"[]",{"VOID":1725},"Shaffer LG: American College of Medical Genetics guideline on the cytogenetic evaluation of the individual with developmental delay or mental retardation. Genet Med. 2005, 7: 650-654. 10.1097\u002F01.gim.0000186545.83160.1e.\nShaffer LG, Bejjani BA, Torchia B, Kirkpatrick S, Coppinger J, Ballif BC: The identification of microdeletion syndromes and other chromosome abnormalities: cytogenetic methods of the past, new technologies for the future. Am J Med Genet C Semin Med Genet. 2007, 145C: 335-345. 10.1002\u002Fajmg.c.30152.\nStankiewicz P, Beaudet AL: Use of array CGH in the evaluation of dysmorphology, malformations, developmental delay, and idiopathic mental retardation. Curr Opin Genet Dev. 2007, 17: 182-192. 10.1016\u002Fj.gde.2007.04.009.\nZahir F, Friedman JM: The impact of array genomic hybridization on mental retardation research: a review of current technologies and their clinical utility. Clin Genet. 2007, 72: 271-287. 10.1111\u002Fj.1399-0004.2007.00847.x.\nKidd JM, Cooper GM, Donahue WF, et al: Mapping and sequencing of structural variation from eight human genomes. Nature. 2008, 453: 56-64. 10.1038\u002Fnature06862.\nLevy S, Sutton G, Ng PC, et al: The diploid genome sequence of an individual human. PLoS Biol. 2007, 5: e254-10.1371\u002Fjournal.pbio.0050254.\nRedon R, Ishikawa S, Fitch KR, et al: Global variation in copy number in the human genome. Nature. 2006, 444: 444-454. 10.1038\u002Fnature05329.\nWang J, Wang W, Li R, et al: The diploid genome sequence of an Asian individual. Nature. 2008, 456: 60-65. 10.1038\u002Fnature07484.\nWheeler DA, Srinivasan M, Egholm M, et al: The complete genome of an individual by massively parallel DNA sequencing. Nature. 2008, 452: 872-876. 10.1038\u002Fnature06884.\nFriedman JM: High-resolution array genomic hybridization in prenatal diagnosis. Prenat Diagn. 2009, 29: 20-28. 10.1002\u002Fpd.2129.\nManning M, Hudgins L: Use of array-based technology in the practice of medical genetics. Genet Med. 2007, 9: 650-653. 10.1097\u002FGIM.0b013e31814cec3a.\nRodriguez-Revenga L, Mila M, Rosenberg C, Lamb A, Lee C: Structural variation in the human genome: the impact of copy number variants on clinical diagnosis. Genet Med. 2007, 9: 600-606. 10.1097\u002FGIM.0b013e318149e1e3.\nVermeesch JR, Fiegler H, de Leeuw N, et al: Guidelines for molecular karyotyping in constitutional genetic diagnosis. Eur J Hum Genet. 2007, 15: 1105-1114. 10.1038\u002Fsj.ejhg.5201896.\nAradhya S, Cherry AM: Array-based comparative genomic hybridization: clinical contexts for targeted and whole-genome designs. Genet Med. 2007, 9: 553-559. 10.1097\u002FGIM.0b013e318149e354.\nShaikh TH: Oligonucleotide arrays for high-resolution analysis of copy number alteration in mental retardation\u002Fmultiple congenital anomalies. 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J Med Genet. 2006, 43: 180-186. 10.1136\u002Fjmg.2005.032268.\nvan Bon BW, Mefford HC, Menten B, et al: Further delineation of the 15q13 microdeletion and duplication syndromes: a clinical spectrum varying from non-pathogenic to a severe outcome. J Med Genet. 2009, 46: 511-523. 10.1136\u002Fjmg.2008.063412.\nDumitrescu AM, Liao XH, Best TB, Brockmann K, Refetoff S: A novel syndrome combining thyroid and neurological abnormalities is associated with mutations in a monocarboxylate transporter gene. Am J Hum Genet. 2004, 74: 168-175. 10.1086\u002F380999.\nShapiro LJ, Yen P, Pomerantz D, Martin E, Rolewic L, Mohandas T: Molecular studies of deletions at the human steroid sulfatase locus. Proc Natl Acad Sci USA. 1989, 86: 8477-8481. 10.1073\u002Fpnas.86.21.8477.\nDibbens LM, Tarpey PS, Hynes K, et al: X-linked protocadherin 19 mutations cause female-limited epilepsy and cognitive impairment. Nat Genet. 2008, 40: 776-781. 10.1038\u002Fng.149.\nTarpey P, Parnau J, Blow M, et al: Mutations in the DLG3 gene cause nonsyndromic X-linked mental retardation. Am J Hum Genet. 2004, 75: 318-324. 10.1086\u002F422703.\nKleefstra T, Koolen DA, Nillesen WM, et al: Interstitial 2.2 Mb deletion at 9q34 in a patient with mental retardation but without classical features of the 9q subtelomeric deletion syndrome. Am J Med Genet A. 2006, 140: 618-623.\nKoolen DA, Sharp AJ, Hurst JA, et al: Clinical and molecular delineation of the 17q21.31 microdeletion syndrome. J Med Genet. 2008, 45: 710-720. 10.1136\u002Fjmg.2008.058701.\nRauch A, Zink S, Zweier C, et al: Systematic assessment of atypical deletions reveals genotype-phenotype correlation in 22q11.2. J Med Genet. 2005, 42: 871-876. 10.1136\u002Fjmg.2004.030619.\nSchorry EK, Keddache M, Lanphear N, et al: Genotype-phenotype correlations in Rubinstein-Taybi syndrome. Am J Med Genet A. 2008, 146A: 2512-2519. 10.1002\u002Fajmg.a.32424.\nPatel KG, Liu C, Cameron PL, Cameron RS: Myr 8, a novel unconventional myosin expressed during brain development associates with the protein phosphatase catalytic subunits 1alpha and 1gamma1. J Neurosci. 2001, 21: 7954-7968.\nThomas S, Ritter B, Verbich D, et al: Intersectin regulates dendritic spine development and somatodendritic endocytosis but not synaptic vesicle recycling in hippocampal neurons. J Biol Chem. 2009, 284: 12410-12419. 10.1074\u002Fjbc.M809746200.\nBruno DL, Ganesamoorthy D, Schoumans J, et al: Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice. 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Adv Hum Genet. 1988, 17: 141-199.\nGarcia-Minaur S, Fantes J, Murray RS, et al: A novel atypical 22q11.2 distal deletion in father and son. J Med Genet. 2002, 39: E62-10.1136\u002Fjmg.39.10.e62.\nAlvarez E, Zhou W, Witta SE, Freed CR: Characterization of the Bex gene family in humans, mice, and rats. Gene. 2005, 357: 18-28. 10.1016\u002Fj.gene.2005.05.012.\nBassi MT, Ramesar RS, Caciotti B, et al: X-linked late-onset sensorineural deafness caused by a deletion involving OA1 and a novel gene containing WD-40 repeats. Am J Hum Genet. 1999, 64: 1604-1616. 10.1086\u002F302408.\nChomez P, De Backer O, Bertrand M, De Plaen E, Boon T, Lucas S: An overview of the MAGE gene family with the identification of all human members of the family. Cancer Res. 2001, 61: 5544-5551.\nYen PH, Ellison J, Salido EC, Mohandas T, Shapiro L: Isolation of a new gene from the distal short arm of the human X chromosome that escapes X-inactivation. 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Genomics. 1997, 46: 397-408. 10.1006\u002Fgeno.1997.5052.\nThe pre-publication history for this paper can be accessed 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comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.",{"EN":2065},"Analysis of disease comorbidity patterns in a large-scale China population",{"VOID":2067},"[\"16111748770688034398\"]",{"VOID":2069},"Capobianco E, Lio P. Comorbidity: a multidimensional approach. Trends Mol Med. 2013;19(9):515–21.\nRadner H, Yoshida K, Smolen JS, et al. multimorbidity and rheumatic conditions-enhancing the concept of comorbidity. Nature reviews. Rheumatology. 2014;10(4):252.\nRubioperez C, Guney E, Aguilar D, et al. Genetic and functional characterization of disease associations explains comorbidity. Sci Rep. 2017;7(1):6207.\nHu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet. 2016;17(10):615–29.\nBragina EY, Freidin MB, Babuskina NP, et al. The analysis of associations between cytokine network genes and inverse co-morbidity of ronchial asthma and tuberculosis. Biomed Genet Genom. 2016;1(5):Z2–4.\nSteven M, Haffner, Lehto S, Tapani R, et al. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med. 1998;339(4):229–34.\nWeiner DE, Tighiouart H, Stark PC, et al. Sarnak, kidney disease as a risk factor for recurrent cardiovascular disease and mortality. Am J Kidney Dis. 2004;44(2):198–206.\nStarfield B, Lemke KW, Bernhardt T, et al. Comorbidity: implications for the importance of primary care in ‘case’ management. Ann Fam Med. 2003;1(1):8–14.\nStruijs JN, Baan CA, Schellevis FG, et al. Comorbidity in patients with diabetes mellitus:impact on medical health care utilization. BMC Health Serv Res. 2006;6(1):84.\nGijsen R, Hoeymans N, Schellevis FG, et al. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54(7):661–74.\nLevin A, Djurdjev O, Barrett B, Thompson C, et al. Cardiovascular disease in patients with chronic kidney disease: getting to the heart of the matter. Am J Kidney Dis. 2001;38(6):1398–407.\nVon Lueder TG, Atar D. Comorbidities and polypharmacy. Heart Fail Clin. 2014;10:367–72.\nHe F, Zhu G, Wang YY, et al. PCID: a novel approach for predicting disease comorbidity by integrating multi-scale data. IEEE\u002FACM Transact Comput Biol Bioinf. 2016;14:1.\nChen H, Zhang Y, Wu D, et al. Comorbidity in adult patients hospitalized with type 2 diabetes in Northeast China: an analysis of hospital discharge data from 2002 to 2013. Biomed Res Int. 2016;2016(11):1–9.\nHidalgo CA, Blumm N, Barabási A, et al. A dynamic network approach for the study of human phenotypes. PLoS Comput Biol. 2009;5(4):e1000353.\nPark J, Lee D, Christakis NA, et al. The impact of cellular networks on disease comorbidity. Mol Syst Biol. 2009;5:262.\nChen Y, Xu R. Network Analysis of Human Disease Comorbidity Patterns Based on Large-scale Data Mining. In: International Symposium on Bioinformatics Research and Applications; 2014. p. 243–54.\nShen Z, Bao W-Z, et al. Recurrent neural network for predicting transcription factor binding sites. Sci Rep. 2018;8:15270.\nYi H-C, You Z-H, et al. A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information. Mol Ther Nucleic Acids. 2018;11:337–44.\nDeng S-P, Lin Z, et al. Predicting hub genes associated with cervical cancer through gene co-expression networks. IEEE\u002FACM Trans Comput Biol Bioinform. 2016;13(1):27–35.\nOrganization, W H. ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th Rev. World Health Org. 1992;56(3):65.\nRappaport N, Nativ N, Stelzer G, et al. MalaCards: an integrated compendium for diseases and their annotation. Database (Oxford). 2013;2013(8):bat018.\nKanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27(1):29–34.\nHan J, Pei J, Yin Y. Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 2000;29(2):1–12.\nNewman MEJ. The structure and function of complex networks. SIAM Rev. 2003;45:167–256.\nRavasz E, Barabási AL. Hierarchical organization in complex networks. Phys Rev E. 2003;67(2):026112.\nChaturvedi P, Dhara M, Arora D. Community detection in complex network via BGLL algorithm. Int J Comp Appl. 2012;48(1):32–42.\nPham TQ, Wang JJ, Rochtchina E, et al. Systemic and ocular comorbidity of cataract surgical patients in a western Sydney public hospital. Clin Exp Ophthalmol. 2004;32(4):383–7.\nLiu Y, Congdon NG, Fan H, et al. Ocular comorbidities among cataract-operated patients in rural China: the caring is hip Study of Cataract Outcomes and Uptake of Services (SCOUTS). Ophthalmology. 2007;114(11):47–52.\nEvans JM, Newton RW, Ruta DA, et al. Socio-economic status, obesity and prevalence of Type 1 and Type 2 diabetes mellitus. Diabet Med. 2000;17(6):478.\nDzudie A, Kengne AP, Mbahe S, et al. Chronic heart failure, selected risk factors and co-morbidities among adults treated for hypertension in a cardiac referral hospital in Cameroon. Eur J Heart Fail. 2008;10:367–72.\nConti CR. Diabetes, hypertension, and cardiovascular disease. Clin Cardiol. 2001;24(1):1.\nChannanath AM, Farran B, Behbehani K, et al. State of Diabetes,Hypertension, and Comorbidity in Kuwait: Showcasing the Trends as Seen in Native Versus Expatriate Populations. Diabetes Care. 2013;36:E75.\nTripathy JP, Thakur JS, Jeet G, et al. Prevalence and determinants of comorbid diabetes and hypertension: Evidence from non communicable disease risk factor STEPS survey, India. Diabetes Metab Syndr. 2017;11(1):S459–65.\nSarafidis PA, Li S, Chen SC, et al. Hypertension awareness, treatment, and control in chronic kidney disease. Am J Med. 2008;121:332–40.\nLukas A, Kumbein F, Temml C, et al. Body mass index is the main risk factor for arterial hypertension in young subjects without major comorbidity. Eur J Clin Investig. 2003;33:223–30.\nUretsky S, Messerli FH, Bangalore S, et al. Obesity paradox in patients with hypertension and coronary artery disease. Am J Med. 2007;120:863–70.\nSun G, Huang G. Treatment strategy of hypertension with heart failure. Adv Cardiovasc Dis. 2016;37(2):201–4 (In Chinese).\nGao Y, Wei Q. Hypertensive ophthalmopathy. Int J Ophthalmol. 2008;8(7):1454–7 (In Chinese).\nYi W, Wei W, Liu Y. Discussion on the experience of applying traditional Chinese medicine to psychiatric patients with palpitation syndrome. Medical Frontier. 2014;5:379 (In Chinese).\nDe Simone G. The difficult clinical management of the combination of hypertension with aortic stenosis. J Hypertens. 2010;28(2):234–6.\nCao X, Ma J. Influence of hypertension on diagnosis and treatment of aortic stenosis and countermeasures. J Cardiovasc Surg. 2016;5(2):24–8 (In Chinese).\nSokal J, Messias E, Dickerson FB, et al. Comorbidity of medical illnesses among adults with serious mental illness who are receiving community psychiatric services. J Nerv Ment Dis. 2004;192(6):421–7.\nLiu J, Ma J, Wang J, et al. Comorbidity analysis according to sex and age in hypertension patients in China. Int J Med Sci. 2016;13(2):99–107.",{"VOID":2071},"10.1186\u002Fs12920-019-0629-x","2024-06-26T16:01:42.352+00:00","https:\u002F\u002Fbmcmedgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12920-019-0629-x",[2075,2090,2103,2128,2143,2156,2171,2186,2199],{"id":2076,"sortIndex":19,"researcher":18,"roles":2077,"affiliations":2078,"properties":2087,"displayName":2089,"givenName":18,"familyName":18},"7694820a-87e5-4e79-bd8a-04d30e292090",[180],[2079],{"id":2080,"sortIndex":19,"affiliation":2081,"properties":18},"ec132fc2-7b90-45ea-80f6-9a86701f28fd",{"id":2080,"createTime":18,"updateTime":18,"relativeEntities":2082,"slug":18,"properties":2083,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2086,"statistic":18},[],{"title":2084},{"VI":2085},"School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 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of the head and neck are highly vascular and usually clinically benign tumors arising in the paraganglia of the autonomic nervous system. A significant number of cases (10–50%) are proven to be familial. Multiple genes encoding subunits of the mitochondrial succinate-dehydrogenase (SDH) complex are associated with hereditary paraganglioma: SDHB, SDHC and SDHD. Furthermore, a hereditary paraganglioma family has been identified with linkage to the PGL2 locus on 11q13. No SDH genes are known to be located in the 11q13 region, and the exact gene defect has not yet been identified in this family. We have performed a RNA expression microarray study in sporadic, SDHD- and PGL2-linked head and neck paragangliomas in order to identify potential differences in gene expression leading to tumorigenesis in these genetically defined paraganglioma subgroups. We have focused our analysis on pathways and functional gene-groups that are known to be associated with SDH function and paraganglioma tumorigenesis, i.e. metabolism, hypoxia, and angiogenesis related pathways. We also evaluated gene clusters of interest on chromosome 11 (i.e. the PGL2 locus on 11q13 and the imprinted region 11p15). We found remarkable similarity in overall gene expression profiles of SDHD -linked, PGL2-linked and sporadic paraganglioma. The supervised analysis on pathways implicated in PGL tumor formation also did not reveal significant differences in gene expression between these paraganglioma subgroups. Moreover, we were not able to detect differences in gene-expression of chromosome 11 regions of interest (i.e. 11q23, 11q13, 11p15). The similarity in gene-expression profiles suggests that PGL2, like SDHD, is involved in the functionality of the SDH complex, and that tumor formation in these subgroups involves the same pathways as in SDH linked paragangliomas. We were not able to clarify the exact identity of PGL2 on 11q13. The lack of differential gene-expression of chromosome 11 genes might indicate that chromosome 11 loss, as demonstrated in SDHD-linked paragangliomas, is an important feature in the formation of paragangliomas regardless of their genetic background.",{"EN":2279},"Similar gene expression profiles of sporadic, PGL2-, and SDHD-linked paragangliomas suggest a common pathway to tumorigenesis",{"VOID":2281},"[\"9524709044380523885\"]",{"VOID":2283},"Baysal BE, Ferrell RE, Willett-Brozick JE, Lawrence EC, Myssiorek D, Bosch A, et al: Mutations in SDHD, a mitochondrial complex II gene, in hereditary paraganglioma. Science. 2000, 287: 848-851. 10.1126\u002Fscience.287.5454.848.\nAstuti D, Latif F, Dallol A, Dahia PLM, Douglas F, George E, et al: Gene mutations in the succinate dehydrogenase subunit SDHB cause susceptibility to familial pheochromocytoma and to familial paraganglioma. Am J Hum Genet. 2001, 69: 49-54. 10.1086\u002F321282.\nNiemann S, Muller U: Mutations in SDHC cause autosomal dominant paraganglioma, type 3. Nat Genet. 2000, 26: 268-270. 10.1038\u002F81551.\nTaschner PE, Jansen JC, Baysal BE, Bosch A, Rosenberg EH, Brocker-Vriends AH, et al: Nearly all hereditary paragangliomas in the Netherlands are caused by two founder mutations in the SDHD gene. Genes Chromosomes Cancer. 2001, 31: 274-281. 10.1002\u002Fgcc.1144.\nMariman EC, Van Beersum SE, Cremers CW, Van Baars FM, Ropers HH: Analysis of a 2nd Family with Hereditary Nonchromaffin Paragangliomas Locates the Underlying Gene at the Proximal Region of Chromosome-11Q. Hum Genet. 1993, 91: 357-361. 10.1007\u002FBF00217356.\nVan Houtum WH, Corssmit EP, Douwes Dekker PB, Jansen JC, van der Mey AG, Bröcker-Vriends AH, et al: Increased prevalence of catecholamine excess and phaeochromocytomas in a well-defined Dutch population with SDHD-linked head and neck paragangliomas. Eur J Endocrinol. 2005, 52: 87-94. 10.1530\u002Feje.1.01833.\nAstuti D, Latif F, Dallol A, Dahia PLM, Douglas F, George E, et al: Gene Mutations in the Succinate Dehydrogenase Subunit SDHB Cause Susceptibility to Familial Pheochromocytoma and to Familial Paraganglioma. Am J Hum Genet. 2001, 69: 49-54. 10.1086\u002F321282.\nAstuti D, Hart-Holden N, Latif F, Lalloo F, Black GC, Lim C, et al: Genetic analysis of mitochondrial complex II subunits SDHD, SDHB and SDHC in paraganglioma and phaeochromocytoma susceptibility. Clin Endocrinol (Oxf). 2003, 59 (6): 728-33. 10.1046\u002Fj.1365-2265.2003.01914.x.\nDahia PL, Ross KN, Wright ME, Hayashida CY, Santagata S, Barontini M, et al: A HIF1 alpha regulatory loop links hypoxia and mitochondrial signals in pheochromocytomas. Plos Genet. 2005, 1: 72-80. 10.1371\u002Fjournal.pgen.0010008.\nHensen EF, Jordanova ES, van Minderhout IJ, Hogendoorn PC, Taschner PE, Mey van der AG, et al: Somatic loss of maternal chromosome 11 causes parent-of-origin-dependent inheritance in SDHD-linked paraganglioma and phaeochromocytoma families. Oncogene. 2004, 23: 4076-4083. 10.1038\u002Fsj.onc.1207591.\nBayley JP, van Minderhout I, Weiss MM, Jansen JC, Oomen PHN, Menko FH, et al: Mutation analysis of SDHB and SDHC: novel germline mutations in sporadic head and neck paraganglioma and familial paraganglioma and\u002For pheochromocytoma. BMC Med Genet. 2006, 7: 1-10.1186\u002F1471-2350-7-1.\nBayley JP, Grimbergen AE, Van Bunderen PA, Van der Wielen M, Kunst HP, Lenders JW, et al: The first Dutch SDHB founder deletion in paraganglioma – pheochromocytoma patients. BMC Med Genet. 2009, 10: 34-10.1186\u002F1471-2350-10-34.\nMRC Holland website. [http:\u002F\u002Fwww.mrc-holland.com]\nAffymetrix website – manuals. 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Stat Appl Genet Mol Biol. 2004, 3: Article 3.\nGoeman JJ, Geer van de SA, de Kort F, van Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome. Bioinformatics. 2004, 20: 93-99. 10.1093\u002Fbioinformatics\u002Fbtg382.\nGSEA website of the Broad institute. [http:\u002F\u002Fwww.broad.mit.edu\u002Fgsea]\nMootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al: PGC-1 alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34: 267-273. 10.1038\u002Fng1180.\nGoeman JJ, Buhlmann P: Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007, 23: 980-987. 10.1093\u002Fbioinformatics\u002Fbtm051.\nManoli T, Gretz N, Grone HJ, Kenzelmann M, Eils R, Brors B: Group testing for pathway analysis improves comparability of different microarray datasets. Bioinformatics. 2006, 22: 2500-2506. 10.1093\u002Fbioinformatics\u002Fbtl424.\nGoeman JJ, Geer van de SA, van Houwelingen HC: Testing against a high dimensional alternative. J Royal Stat Soc B Stat Meth. 2006, 68: 477-493. 10.1111\u002Fj.1467-9868.2006.00551.x.\nOgata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research. 1999, 27: 29-34. 10.1093\u002Fnar\u002F27.1.29.\nKEGG website: Kyoto Encyclopedia of Genes and Genomes. [http:\u002F\u002Fwww.genome.jp\u002Fkegg]\nBiocarta website. [http:\u002F\u002Fwww.biocarta.com]\nBenjamini Y, Hochberg Y: Controlling the False Discovery Rate – A Practical and Powerful Approach to Multiple Testing. J Royal Stat Soc B. 1995, 57: 289-300.\nVanbaars FM, Cremers CWRJ, Vandenbroek P, Veldman JE: Familiar Non-Chromaffinic Paragangliomas (Glomus Tumors) – Clinical and Genetic-Aspects (abridged). Acta Otolaryngol. 1981, 91: 589-593. 10.3109\u002F00016488109138545.\nVandermey AGL, Frijns JHM, Cornelisse CJ, Brons EN, Vandulken H, Terpstra HL, et al: Does Intervention Improve the Natural Course of Glomus Tumors – A Series of 108 Patients Seen in A 32-Year Period. Ann Otol Rhinol Laryngol. 1992, 101: 635-642.\nBenn DE, Gimenez-Roqueplo AP, Reilly JR, Bertherat J, Burgess J, Byth K, et al: Clinical presentation and penetrance of pheochromocytoma\u002Fparaganglioma syndromes. J Clin Endocrinol Metab. 2006, 91: 827-836. 10.1210\u002Fjc.2005-1862.\nNeumann HP, Pawlu C, Peczkowska M, Bausch B, McWhinney SR, Muresan M, et al: Distinct clinical features of paraganglioma syndromes associated with SDHB and SDHD gene mutations. JAMA. 2004, 292: 943-951. 10.1001\u002Fjama.292.8.943.\nLack EE, Cubilla AL, Woodruff JM: Paragangliomas of the Head and Neck Region – Pathologic-Study of Tumors from 71 Patients. Hum Pathol. 1979, 10: 191-218. 10.1016\u002FS0046-8177(79)80008-8.\nDekker PB, Corver WE, Hogendoom PC, Mey van der AG, Cornelisse CJ: Multiparameter DNA flow-sorting demonstrates diploidy and SDHD wild-type gene retention in the sustentacular cell compartment of head and neck paragangliomas: chief cells are the only neoplastic component. J Pathol. 2004, 202: 456-462. 10.1002\u002Fpath.1535.\nDekker PB, Hogendoorn PC, Kuipers-Dijkshoorn N, Prins FA, van Duinen SG, Taschner PE, et al: SDHD mutations in head and neck paragangliomas result in destabilization of complex II in the mitochondrial respiratory chain with loss of enzymatic activity and abnormal mitochondrial morphology. J Pathol. 2003, 201: 480-486. 10.1002\u002Fpath.1461.\nPollard PJ, El Bahrawy M, Poulsom R, Elia G, Killick P, Kelly G, et al: Expression of HIF-1 alpha, HIF-2 alpha (EPAS1), and their target genes in paraganglioma and pheochromocytoma with VHL and SDH mutations. J Clin Endocrinol Metab. 2006, 91: 4593-4598. 10.1210\u002Fjc.2006-0920.\nPollard PJ, Briere JJ, Alam NA, Barwell J, Barclay E, Wortham NC, et al: Accumulation of Krebs cycle intermediates and over-expression of HIF1 alpha in tumours which result from germline FH and SDH mutations. Hum Mol Genet. 2005, 14: 2231-2239. 10.1093\u002Fhmg\u002Fddi227.\nGimenez-Roqueplo AP, Favier J, Rustin P, Mourad JJ, Plouin PF, Corvol P, et al: The R22X mutation of the SDHD gene in hereditary paraganglioma abolishes the enzymatic activity of complex II in the mitochondrial respiratory chain and activates the hypoxia pathway. Am J Hum Genet. 2001, 69: 1186-1197. 10.1086\u002F324413.\nSelak MA, Armour SM, MacKenzie ED, Boulahbel H, Watson DG, Mansfield KD, et al: Succinate links TCA cycle dysfunction to oncogenesis by inhibiting HIF-alpha prolyl hydroxylase. Cancer Cell. 2005, 7: 77-85. 10.1016\u002Fj.ccr.2004.11.022.\nHirota K, Semenza GL: Regulation of angiogenesis by hypoxia-inducible factor 1. Crit Rev Oncol Hematol. 2006, 59: 15-26. 10.1016\u002Fj.critrevonc.2005.12.003.\nChoi KS, Bae MK, Jeong JW, Moon HE, Kim KW: Hypoxia-induced angiogenesis during carcinogenesis. J Biochem Mol Biol. 2003, 36: 120-127.\nStruycken PM, Cremers CW, Mariman EC, Joosten FB, Bleker RJ: Glomus tumours and genomic imprinting: Influence of inheritance along the paternal or maternal line. Clin Otolaryngol. 1997, 22: 71-76. 10.1046\u002Fj.1365-2273.1997.00884.x.\nvan der mey AG, Maaswinkelmooy PD, Cornelisse CJ, Schmidt PH, van de kamp JJ: Genomic Imprinting in Hereditary Glomus Tumors – Evidence for New Genetic Theory. Lancet. 1989, 2: 1291-1294. 10.1016\u002FS0140-6736(89)91908-9.\nPigny P, Vincent A, Cardot BC, Bertrand M, de Montpreville VT, Crepin M, et al: Paraganglioma after maternal transmission of a succinate dehydrogenase gene mutation. J Clin Endocrinol Metab. 2008, 93: 1609-1615. 10.1210\u002Fjc.2007-1989.\nDannenberg H, de Krijger RR, Zhao JM, Speel EJ, Saremaslani P, Dinjens WN , et al: Differential loss of chromosome 11q in familial and sporadic parasympathetic paragangliomas detected by comparative genomic hybridization. Am J Pathol. 2001, 158: 1937-1942.\nThe pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1755-8794\u002F2\u002F25\u002Fprepub",{"VOID":2285},"10.1186\u002F1755-8794-2-25","2024-05-14T06:41:17.156+00:00","https:\u002F\u002Fbmcmedgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1755-8794-2-25",[2289,2314,2331,2346,2359,2372,2387,2402],{"id":2290,"sortIndex":19,"researcher":18,"roles":2291,"affiliations":2292,"properties":2309,"displayName":2311,"givenName":18,"familyName":18},"147aec9a-bf72-4cd4-81da-645a4bc98a1e",[180],[2293,2301],{"id":2294,"sortIndex":19,"affiliation":2295,"properties":18},"ba4f66bc-ecc2-4a18-a899-402817841a7a",{"id":2294,"createTime":18,"updateTime":18,"relativeEntities":2296,"slug":18,"properties":2297,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2300,"statistic":18},[],{"title":2298},{"VI":2299},"Department 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dermatitis (AD) is a common inflammatory skin disease with limited treatment options. Several microarray experiments have been conducted on lesional\u002FLS and non-lesional\u002FNL AD skin to develop a genomic disease phenotype. Although these experiments have shed light on disease pathology, inter-study comparisons reveal large differences in resulting sets of differentially expressed genes (DEGs), limiting the utility of direct comparisons across studies. We carried out a meta-analysis combining 4 published AD datasets to define a robust disease profile, termed meta-analysis derived AD (MADAD) transcriptome. This transcriptome enriches key AD pathways more than the individual studies, and associates AD with novel pathways, such as atherosclerosis signaling (IL-37, selectin E\u002FSELE). We identified wide lipid abnormalities and, for the first time in vivo, correlated Th2 immune activation with downregulation of key epidermal lipids (FA2H, FAR2, ELOVL3), emphasizing the role of cytokines on the barrier disruption in AD. Key AD “classifier genes” discriminate lesional from nonlesional skin, and may evaluate therapeutic responses. Our meta-analysis provides novel and powerful insights into AD disease pathology, and reinforces the concept of AD as a systemic disease.",{"EN":2481},"Meta-analysis derived atopic dermatitis (MADAD) transcriptome defines a robust AD signature highlighting the involvement of atherosclerosis and lipid metabolism pathways",{"VOID":1723},{"VOID":2484},"Boguniewicz M, Leung DYM. Atopic dermatitis: a disease of altered skin barrier and immune dysregulation. Immunol Rev. 2011;242(1):233–46.\nLeung DYM. New insights into atopic dermatitis: role of skin barrier and immune dysregulation. Allergol Int. 2013;62(2):151–61.\nDhingra N, Suarez-Farinas M, Fuentes-Duculan J, Gittler JK, Shemer A, Raz A, et al. Attenuated neutrophil axis in atopic dermatitis compared to psoriasis reflects TH17 pathway differences between these diseases. J Allergy Clin Immunol. 2013;132(2):498–501.e3.\nMalajian D, & Guttman-Yassky E. New pathogenic and therapeutic paradigms in atopic dermatitis. Cytokine. 2015;73(2):311-318.\nN & Guttman-Yassky E. The translational revolution and use of biologics in patients with inflammatory skin diseases. Journal of Allergy and Clinical Immunology. 2015;135(2):324-336.\nOlsson M, Broberg A, Jernås M, Carlsson L, Rudemo M, Suurküla M, et al. Increased expression of aquaporin 3 in atopic eczema. Allergy. 2006;61(9):1132–7.\nGuttman-Yassky E, Suárez-Fariñas M, Chiricozzi A, Nograles KE, Shemer A, Fuentes-Duculan J, et al. Broad defects in epidermal cornification in atopic dermatitis identified through genomic analysis. J Allergy Clin Immunol. 2009;124(6):1235–1244.e58.\nElias PM, Hatano Y, Williams ML. Basis for the barrier abnormality in atopic dermatitis: Outside-inside-outside pathogenic mechanisms. J Allergy Clin Immunol. 2008;121(6):1337–43.\nGittler JK, Shemer A, Suárez-Fariñas M, Fuentes-Duculan J, Gulewicz KJ, Wang CQF, et al. Progressive activation of T(H)2\u002FT(H)22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis. J Allergy Clin Immunol. 2012;130(6):1344–54.\nBeck L, Thaçi D, Hamilton JD, Graham NM, Bieber T, Rocklin R, et al. Dupilumab Treatment in Adults with Moderate-to-Severe Atopic Dermatitis. N Engl J Med. 2014;371(2):130–9.\nTintle S, Shemer A, Suárez-Fariñas M, Fujita H, Gilleaudeau P, Sullivan-Whalen M, et al. Reversal of atopic dermatitis with narrow-band UVB phototherapy and biomarkers for therapeutic response. J Allergy Clin Immunol. 2011;128(3):583–93.e1–4.\nKhattri S, Shemer A, Rozenblit M, Dhingra N, Czarnowicki T, Finney R, & Guttman-Yassky E. Cyclosporine in patients with atopic dermatitis modulates activated inflammatory pathways and reverses epidermal pathology. Journal of Allergy and Clinical Immunology. 2014;133(6):1626-634.\nJensen JM, Pfeiffer S, Witt M, Bräutigam M, Neumann C, Weichenthal M, et al. Different effects of pimecrolimus and betamethasone on the skin barrier in patients with atopic dermatitis. J Allergy Clin Immunol. 2009;123:1124–33.\nPlager A, Leontovich AA, Henke SA, Davis MDP, McEvoy MT, Sciallis GF, et al. Early cutaneous gene transcription changes in adult atopic dermatitis and potential clinical implications. Exp Dermatol. 2007;16(1):28–36.\nSuárez-Fariñas M, Tintle SJ, Shemer A, Chiricozzi A, Nograles K, Cardinale I, et al. Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities. J Allergy Clin Immunol. 2011;127(4):954–64.e1–4.\nZaba LC, Suárez-Fariñas M, Fuentes-Duculan J, Nograles KE, Guttman-Yassky E, Cardinale I, et al. Effective treatment of psoriasis with etanercept is linked to suppression of IL-17 signaling, not immediate response TNF genes. J Allergy Clin Immunol. 2009;124(5):1022–10.e1–395.\nKrueger JG, Fretzin S, Suárez-Fariñas M, Haslett PA, Phipps KM, Cameron GS, et al. IL-17A is essential for cell activation and inflammatory gene circuits in subjects with psoriasis. J Allergy Clin Immunol. 2012;130(1):1–18.\nHamilton JD, Suárez-Fariñas M, Dhingra N, Cardinale I, Li X, Kostic A, et al. Dupilumab improves the molecular signature in skin of patients with moderate-to-severe atopic dermatitis. J Allergy Clin Immunol. 2014;134(6):1293–300.\nJiang K, Frank MB, Chen Y, Osban J, Jarvis JN. Genomic characterization of remission in juvenile idiopathic arthritis. Arthritis Res Ther. 2013;15(4):R100.\nCahan P, Rovegno F, Mooney D, Newman JC, St Laurent G, McCaffrey TA. Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. Gene. 2007;401(1–2):12–8.\nRamasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9):e184.\nZakharkin SO, Kim K, Mehta T, Chen L, Barnes S, Scheirer KE, et al. Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics. 2005;6:214.\nSuárez-Fariñas M, Noggle S, Heke M, Hemmati-Brivanlou A, Magnasco MO. Comparing independent microarray studies: the case of human embryonic stem cells. BMC Genomics. 2005;6:99.\nSuárez-Fariñas M, Lowes MA, Zaba LC, Krueger JG. Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA). PLoS One. 2010;5(4):e10247.\nTian S, Krueger JG, Li K, Jabbari A, Brodmerkel C, Lowes MA, et al. Meta-analysis derived (MAD) transcriptome of psoriasis defines the ‘core’ pathogenesis of disease. PLoS One. 2012;7(9):e44274.\nChang L-C, Lin H-M, Sibille E, Tseng GC. 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Staphylococcal exotoxins are strong inducers of IL-22: A potential role in atopic dermatitis. 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