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Am J Med Genet A. 2013;161A(1):114–9.\nReis LM, Maheshwari M, Capasso J, Atilla H, Dudakova L, Thompson S, et al. Axenfeld-Rieger syndrome: more than meets the eye. J Med Genet. 2022.\nMaeso I, Irimia M, Tena JJ, Gonzalez-Perez E, Tran D, Ravi V, et al. An ancient genomic regulatory block conserved across bilaterians and its dismantling in tetrapods by retrogene replacement. Genome Res. 2012;22(4):642–55.\nGhiasvand NM, Rudolph DD, Mashayekhi M, Brzezinski JA, Goldman D, Glaser T. Deletion of a remote enhancer near ATOH7 disrupts retinal neurogenesis, causing NCRNA disease. Nat Neurosci. 2011;14(5):578–86.\nShort PJ, McRae JF, Gallone G, Sifrim A, Won H, Geschwind DH, et al. De novo mutations in regulatory elements in neurodevelopmental disorders. Nature. 2018;555(7698):611–6.\nVolkmann BA, Zinkevich NS, Mustonen A, Schilter KF, Bosenko DV, Reis LM, et al. Potential novel mechanism for Axenfeld-Rieger syndrome: deletion of a distant region containing regulatory elements of PITX2. Invest Ophthalmol Vis Sci. 2011;52(3):1450–9.\nProtas ME, Weh E, Footz T, Kasberger J, Baraban SC, Levin AV, et al. Mutations of conserved non-coding elements of PITX2 in patients with ocular dysgenesis and developmental glaucoma. Hum Mol Genet. 2017;26(18):3630–8.\nCano-Gamez E, Trynka G. From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front Genet. 2020;11:424.\nBailey JN, Loomis SJ, Kang JH, Allingham RR, Gharahkhani P, Khor CC, et al. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma. Nat Genet. 2016;48(2):189–94.\nAlipanahi B, Hormozdiari F, Behsaz B, Cosentino J, McCaw ZR, Schorsch E, et al. Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology. Am J Hum Genet. 2021;108(7):1217–30.\nLetelier J, de la Calle-Mustienes E, Pieretti J, Naranjo S, Maeso I, Nakamura T, et al. A conserved Shh cis-regulatory module highlights a common developmental origin of unpaired and paired fins. Nat Genet. 2018;50(4):504–9.\nTopczewska JM, Topczewski J, Solnica-Krezel L, Hogan BL. Sequence and expression of zebrafish foxc1a and foxc1b, encoding conserved forkhead\u002Fwinged helix transcription factors. Mech Dev. 2001;100(2):343–7.\nSkarie JM, Link BA. FoxC1 is essential for vascular basement membrane integrity and hyaloid vessel morphogenesis. Invest Ophthalmol Vis Sci. 2009;50(11):5026–34.\nLi J, Yue Y, Dong X, Jia W, Li K, Liang D, et al. Zebrafish foxc1a plays a crucial role in early somitogenesis by restricting the expression of aldh1a2 directly. J Biol Chem. 2015;290(16):10216–28.\nYue Y, Jiang M, He L, Zhang Z, Zhang Q, Gu C, et al. The transcription factor Foxc1a in zebrafish directly regulates expression of nkx2.5, encoding a transcriptional regulator of cardiac progenitor cells. J Biol Chem. 2018;293(2):638–50.\nXu P, Balczerski B, Ciozda A, Louie K, Oralova V, Huysseune A, et al. Fox proteins are modular competency factors for facial cartilage and tooth specification. Development. 2018;145(12):dev165498.\nFerre-Fernandez JJ, Sorokina EA, Thompson S, Collery RF, Nordquist E, Lincoln J, et al. Disruption of foxc1 genes in zebrafish results in dosage-dependent phenotypes overlapping Axenfeld-Rieger syndrome. Hum Mol Genet. 2020;29(16):2723–35.\nDavis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 2018;46(D1):D794–801.\nHaliburton GD, McKinsey GL, Pollard KS. Disruptions in a cluster of computationally identified enhancers near FOXC1 and GMDS may influence brain development. Neurogenetics. 2016;17(1):1–9.\nSoules KA, Link BA. Morphogenesis of the anterior segment in the zebrafish eye. BMC Dev Biol. 2005;5:12.\nGray MP, Smith RS, Soules KA, John SW, Link BA. The aqueous humor outflow pathway of zebrafish. Invest Ophthalmol Vis Sci. 2009;50(4):1515–21.\nSeese SE, Deml B, Muheisen S, Sorokina E, Semina EV. Genetic disruption of zebrafish mab21l1 reveals a conserved role in eye development and affected pathways. Dev Dyn. 2021;250(8):1056–73.\nLawson ND, Weinstein BM. In vivo imaging of embryonic vascular development using transgenic zebrafish. Dev Biol. 2002;248(2):307–18.\nKaufman R, Weiss O, Sebbagh M, Ravid R, Gibbs-Bar L, Yaniv K, et al. Development and origins of zebrafish ocular vasculature. BMC Dev Biol. 2015;15:18.\nUdvadia AJ. 3.6 kb genomic sequence from Takifugu capable of promoting axon growth-associated gene expression in developing and regenerating zebrafish neurons. Gene Expr Patterns. 2008;8(6):382–8.\nDiekmann H, Kalbhen P, Fischer D. Characterization of optic nerve regeneration using transgenic zebrafish. Front Cell Neurosci. 2015;9:118.\nGoetz KE, Reeves MJ, Gagadam S, Blain D, Bender C, Lwin C, et al. Genetic testing for inherited eye conditions in over 6,000 individuals through the eyeGENE network. Am J Med Genet C Semin Med Genet. 2020;184(3):828–37.\nShickh S, Mighton C, Uleryk E, Pechlivanoglou P, Bombard Y. The clinical utility of exome and genome sequencing across clinical indications: a systematic review. Hum Genet. 2021;140(10):1403–16.\nBergant G, Maver A, Peterlin B. Whole-genome sequencing in diagnostics of selected slovenian undiagnosed patients with rare disorders. Life (Basel). 2021;11(3):205.\nFarh KK, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015;518(7539):337–43.\nQian X, Wang J, Wang M, Igelman AD, Jones KD, Li Y, et al. Identification of deep-intronic splice mutations in a large cohort of patients with inherited retinal diseases. Front Genet. 2021;12: 647400.\nHirsch N, Eshel R, Bar Yaacov R, Shahar T, Shmulevich F, Dahan I, et al. Unraveling the transcriptional regulation of TWIST1 in limb development. PLoS Genet. 2018;14(10): e1007738.\nMcVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE, Raj A, et al. Identification of genetic variants that affect histone modifications in human cells. Science. 2013;342(6159):747–9.\nMartin-Trujillo A, Patel N, Richter F, Jadhav B, Garg P, Morton SU, et al. Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles. PLoS Genet. 2020;16(11): e1009189.\nSouzeau E, Siggs OM, Zhou T, Galanopoulos A, Hodson T, Taranath D, et al. Glaucoma spectrum and age-related prevalence of individuals with FOXC1 and PITX2 variants. Eur J Hum Genet. 2017;25(11):1290.\nIrimia M, Tena JJ, Alexis MS, Fernandez-Minan A, Maeso I, Bogdanovic O, et al. Extensive conservation of ancient microsynteny across metazoans due to cis-regulatory constraints. Genome Res. 2012;22(12):2356–67.\nSeruggia D, Fernandez A, Cantero M, Pelczar P, Montoliu L. Functional validation of mouse tyrosinase non-coding regulatory DNA elements by CRISPR-Cas9-mediated mutagenesis. Nucleic Acids Res. 2015;43(10):4855–67.\nMona B, Villarreal J, Savage TK, Kollipara RK, Boisvert BE, Johnson JE. Positive autofeedback regulation of Ptf1a transcription generates the levels of PTF1A required to generate itch circuit neurons. Genes Dev. 2020;34(9–10):621–36.\nTamm ER. The trabecular meshwork outflow pathways: structural and functional aspects. Exp Eye Res. 2009;88(4):648–55.\nPark DY, Lee J, Park I, Choi D, Lee S, Song S, et al. Lymphatic regulator PROX1 determines Schlemm’s canal integrity and identity. J Clin Invest. 2014;124(9):3960–74.\nDrechsler J, Lee A, Maripudi S, Kueny L, Levin MR, Saeedi OJ, et al. Corneal structural changes in congenital glaucoma. Eye Contact Lens. 2021;48:27–32.\nGarcia-Anton MT, Salazar JJ, de Hoz R, Rojas B, Ramirez AI, Trivino A, et al. Goniodysgenesis variability and activity of CYP1B1 genotypes in primary congenital glaucoma. PLoS ONE. 2017;12(4): e0176386.\nSmith RS, Zabaleta A, Kume T, Savinova OV, Kidson SH, Martin JE, et al. Haploinsufficiency of the transcription factors FOXC1 and FOXC2 results in aberrant ocular development. Hum Mol Genet. 2000;9(7):1021–32.\nFalero-Perez J, Larsen MC, Teixeira LBC, Zhang HF, Lindner V, Sorenson CM, et al. Targeted deletion of Cyp1b1 in pericytes results in attenuation of retinal neovascularization and trabecular meshwork dysgenesis. Trends Dev Biol. 2019;12:1–12.\nThomson BR, Heinen S, Jeansson M, Ghosh AK, Fatima A, Sung HK, et al. A lymphatic defect causes ocular hypertension and glaucoma in mice. J Clin Invest. 2014;124(10):4320–4.\nBuchanan JA, Scherer SW. Contemplating effects of genomic structural variation. Genet Med. 2008;10(9):639–47.\nFlöttmann R, Kragesteen BK, Geuer S, Socha M, Allou L, Sowińska-Seidler A, et al. Noncoding copy-number variations are associated with congenital limb malformation. Genet Med. 2018;20(6):599–607.\nWeirauch MT, Hughes TR. Conserved expression without conserved regulatory sequence: the more things change, the more they stay the same. Trends Genet. 2010;26(2):66–74.\nGilmour DT, Maischein HM, Nusslein-Volhard C. Migration and function of a glial subtype in the vertebrate peripheral nervous system. Neuron. 2002;34(4):577–88.\nKimmel CB, Ballard WW, Kimmel SR, Ullmann B, Schilling TF. Stages of embryonic development of the zebrafish. Dev Dyn. 1995;203(3):253–310.\nSchneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–5.\nLatendresse JR, Warbrittion AR, Jonassen H, Creasy DM. Fixation of testes and eyes using a modified Davidson’s fluid: comparison with Bouin’s fluid and conventional Davidson’s fluid. Toxicol Pathol. 2002;30(4):524–33.\nLivak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8.",{"EN":423},"FOXC1 encodes a forkhead-domain transcription factor associated with several ocular disorders. Correct FOXC1 dosage is critical to normal development, yet the mechanisms controlling its expression remain unknown. Together with FOXQ1 and FOXF2, FOXC1 is part of a cluster of FOX genes conserved in vertebrates. CRISPR-Cas9-mediated dissection of genomic sequences surrounding two zebrafish orthologs of FOXC1 was performed. This included five zebrafish–human conserved regions, three downstream of foxc1a and two remotely upstream of foxf2a\u002Ffoxc1a or foxf2b\u002Ffoxc1b clusters, as well as two intergenic regions between foxc1a\u002Fb and foxf2a\u002Fb lacking sequence conservation but positionally corresponding to the area encompassing a previously reported glaucoma-associated SNP in humans. Removal of downstream sequences altered foxc1a expression; moreover, zebrafish carrying deletions of two or three downstream elements demonstrated abnormal phenotypes including enlargement of the anterior chamber of the eye reminiscent of human congenital glaucoma. Deletions of distant upstream conserved elements influenced the expression of foxf2a\u002Fb or foxq1a\u002Fb but not foxc1a\u002Fb within each cluster. Removal of either intergenic sequence reduced foxc1a or foxc1b expression during late development, suggesting a role in transcriptional regulation despite the lack of conservation at the nucleotide level. Further studies of the identified regions in human patients may explain additional individuals with developmental ocular disorders.",{"EN":425},"CRISPR-Cas9-mediated functional dissection of the foxc1 genomic region in zebrafish identifies critical conserved cis-regulatory elements",{"VOID":427},"10.1186\u002Fs40246-022-00423-x","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40246-022-00423-x",[430,466,478,490],{"id":431,"sortIndex":136,"researcher":18,"roles":432,"affiliations":434,"properties":463},"30a6b189-09b1-40b2-b49e-947c2eaa2d17",[433],"AUTHOR",[435,443,453],{"id":18,"sortIndex":19,"affiliation":436,"properties":18},{"id":437,"createTime":438,"updateTime":438,"relativeEntities":439,"slug":18,"properties":440,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"82c9a3e5-b088-430c-a323-6c66e0787b07","2024-02-15T05:43:38.243+00:00",[],{"title":441},{"VI":442},"Department of Pediatrics and Children’s Research Institute, Medical College of Wisconsin and Children’s Hospital of Wisconsin, Milwaukee, USA",{"id":444,"sortIndex":135,"affiliation":445,"properties":452},"ef0235c5-116b-41ec-aa92-3df8daef0e6a",{"id":446,"createTime":447,"updateTime":447,"relativeEntities":448,"slug":18,"properties":449,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"d329f690-f420-4f22-bc55-22d625fa7418","2024-01-20T19:30:34.211+00:00",[],{"title":450},{"VI":451},"Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, USA",{},{"id":454,"sortIndex":121,"affiliation":455,"properties":462},"a0a4aaa2-c37e-4676-b258-56404d59b233",{"id":456,"createTime":457,"updateTime":457,"relativeEntities":458,"slug":18,"properties":459,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"104e3233-a376-47dc-818c-dee712c576d9","2023-12-06T17:53:40.778+00:00",[],{"title":460},{"VI":461},"Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, USA",{},{"title":464},{"VI":465},"Elena V. Semina",{"id":467,"sortIndex":19,"researcher":18,"roles":468,"affiliations":469,"properties":475},"7aa2b3a5-31cc-4094-aef4-c8716e59e4ed",[433],[470],{"id":18,"sortIndex":19,"affiliation":471,"properties":18},{"id":437,"createTime":438,"updateTime":438,"relativeEntities":472,"slug":18,"properties":473,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":474},{"VI":442},{"title":476},{"VI":477},"Jesús-José Ferre-Fernández",{"id":479,"sortIndex":121,"researcher":18,"roles":480,"affiliations":481,"properties":487},"15a8e02f-afaa-446d-ad06-17c0c4c7e13a",[433],[482],{"id":18,"sortIndex":19,"affiliation":483,"properties":18},{"id":437,"createTime":438,"updateTime":438,"relativeEntities":484,"slug":18,"properties":485,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":486},{"VI":442},{"title":488},{"VI":489},"Samuel Thompson",{"id":491,"sortIndex":135,"researcher":18,"roles":492,"affiliations":493,"properties":499},"c06f5f27-9fb9-40d9-9af0-86e876fd3a7b",[433],[494],{"id":18,"sortIndex":19,"affiliation":495,"properties":18},{"id":437,"createTime":438,"updateTime":438,"relativeEntities":496,"slug":18,"properties":497,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":498},{"VI":442},{"title":500},{"VI":501},"Sanaa Muheisen",{"url":428,"publisher":503,"properties":530},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":504,"slug":10,"properties":505,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":508,"manageAffiliations":509,"indexDatabases":510,"url":18,"thumbnailPath":18,"statistic":525,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":506,"title":507},{"VOID":13},{"EN":15},[],[],[511,518],{"id":97,"indexDatabase":512,"url":110,"indexYears":111,"academicFieldIds":517,"indexDatabaseRanking":117},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":513,"label":514,"description":515,"key":107,"publicationTags":516,"standard":18},[],{"EN":104,"VI":104},{"EN":104,"VI":106},[109],[113,114,115,116],{"id":78,"indexDatabase":519,"url":93,"indexYears":18,"academicFieldIds":524,"indexDatabaseRanking":18},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":520,"label":521,"description":522,"key":89,"publicationTags":523,"standard":18},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95],{"impactFactor":19,"impactFactorByYear":526,"i10Index":132,"i10IndexLast5Year":64,"totalPublication":133,"totalPublicationByYear":527,"totalCitation":140,"totalCitationByYear":528,"totalCitationPerPublication":156,"totalCitationPerPublicationByYear":529,"hindexLast5Year":171,"hindex":171},{"2012":120,"2013":121,"2014":122,"2015":123,"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":130,"2023":131},{"2003":135,"2004":136,"2005":135,"2006":121,"2008":135,"2009":121,"2010":121,"2011":135,"2012":121,"2013":137,"2014":136,"2015":136,"2016":130,"2017":121,"2018":138,"2019":139,"2020":138,"2021":138,"2022":136},{"2003":142,"2004":143,"2006":144,"2008":135,"2009":145,"2010":146,"2011":147,"2012":133,"2013":148,"2014":139,"2015":149,"2016":150,"2017":132,"2018":151,"2019":152,"2020":153,"2021":154,"2022":155},{"2003":142,"2004":158,"2006":159,"2008":135,"2009":160,"2010":161,"2011":147,"2012":162,"2013":163,"2014":164,"2015":139,"2016":165,"2017":166,"2018":167,"2019":168,"2020":169,"2021":166,"2022":170},{"volume":531,"pages":533},{"VOID":532},"16",{"VOID":534},"1-17","2022-10-25",2022,{"id":538,"createTime":539,"updateTime":540,"relativeEntities":541,"slug":542,"properties":543,"entityType":197,"verifyStatus":198,"verifyTime":540,"verifyNote":199,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":552,"fullTextUrl":18,"authors":553,"publicationType":295,"publisherRelationship":646,"citationCount":18,"citationInfo":18,"publishDate":679,"publishYear":680,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":412},"78f5af96-4f39-4fff-8aa0-64237934c302","2023-11-28T22:50:59.951+00:00","2024-12-17T23:55:37.972+00:00",[],"How-to-create-innovation-by-building-the-translation-bridge-from-basic-research-into-medicinal-drugs-an-industrial-perspective",{"references":544,"abstract":546,"title":548,"doi":550},{"VOID":545},"Hughes B: FDA drug approvals. Nature Rev Drug Discov. 2009, 8: 93-96. 10.1038\u002Fnrd2813.\nMullard A: 2011 FDA drug approvals. Nature Rev Drug Discov. 2012, 11: 91-94. 10.1038\u002Fnrd3657.\nFDA: FDA.org. The center for health and wellness. http:\u002F\u002Ffda.org\u002F,",{"EN":547},"The global healthcare industry is undergoing substantial changes and adaptations to the constant decline of approved new medical entities. This decrease in internal research productivity is resulting in a major decline of patent-protected sales (patent cliff) of most of the pharmaceutical companies. Three major global adaptive trends as driving forces to cope with these challenges are evident: cut backs of internal research and development jobs in the western hemisphere (Europe and USA), following the market growth potential of Asia by building up internal or external research and development capabilities there and finally, ‘early innovation hunting’ with an increased focus on identifying and investing in very early innovation sources within academia and small start-up companies. Early innovation hunting can be done by different approaches: increased corporate funding, establishment of translational institutions to bridge innovation, increasing sponsored collaborations and formation of technology hunting groups for capturing very early scientific ideas and concepts. This emerging trend towards early innovation hunting demands special adaptations from both the pharmaceutical industry and basic researchers in academia to bridge the translation into new medicines which deliver innovative medicines that matters to the patient. This opinion article describes the different modalities of cross-fertilisation between basic university or publicly funded institutional research and the applied research and development activities within the pharmaceutical industry. Two key factors in this important translational bridge can be identified: preparation of both partnering organisations to open up for new and sometime disruptive ideas and creation of truly trust-based relationships between the different groups allowing long-term scientific collaborations while acknowledging that value-creating differences are an essential factor for successful collaboration building.",{"EN":549},"How to create innovation by building the translation bridge from basic research into medicinal drugs: an industrial perspective",{"VOID":551},"10.1186\u002F1479-7364-7-5","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1479-7364-7-5",[554,570,586,598,614,630],{"id":555,"sortIndex":136,"researcher":18,"roles":556,"affiliations":557,"properties":567},"45da4fd6-d697-45b8-b46b-4d984174f66c",[433],[558],{"id":18,"sortIndex":19,"affiliation":559,"properties":18},{"id":560,"createTime":561,"updateTime":561,"relativeEntities":562,"slug":563,"properties":564,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"de631854-b2e9-41b5-b9b3-9d229f2baf00","2023-11-28T22:50:59.983+00:00",[],"New-Frontier-Science-Takeda-Pharmaceuticals-Chicago-USA",{"title":565},{"VI":566},"New Frontier Science, Takeda Pharmaceuticals, Chicago, USA",{"title":568},{"VI":569},"Ronald 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Harrison",{"id":587,"sortIndex":206,"researcher":18,"roles":588,"affiliations":589,"properties":595},"bfc0e1d4-e2f0-4530-ba23-d7c40cf94249",[433],[590],{"id":18,"sortIndex":19,"affiliation":591,"properties":18},{"id":560,"createTime":561,"updateTime":561,"relativeEntities":592,"slug":563,"properties":593,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":594},{"VI":566},{"title":596},{"VI":597},"Kevin 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Annu Rev Med. 2006, 57: 119-137. 10.1146\u002Fannurev.med.56.082103.104724.\nBertz RJ, Granneman GR: 'Use of in vitro and in vivo data to estimate the likelihood of metabolic pharmacokinetic interactions'. Clin Pharmacokinet. 1997, 32: 210-258. 10.2165\u002F00003088-199732030-00004.\nEvans WE, Relling MV: 'Pharmacogenomics: Translating functional genomics into rational therapeutics'. Science. 1999, 286: 487-491. 10.1126\u002Fscience.286.5439.487.\nWeinshilboum R: 'Inheritance and drug response'. N Engl J Med. 2003, 348: 529-537. 10.1056\u002FNEJMra020021.\nIngelman-Sundberg M: 'Pharmacogenetics of cytochrome P450 and its applications in drug therapy: The past, present and future'. Trends Pharmacol Sci. 2004, 25: 193-200. 10.1016\u002Fj.tips.2004.02.007.\nKirchheiner J, Nickchen K, Bauer M, Wong ML, et al: 'Pharmacogenetics of antidepressants and antipsychotics: the contribution of allelic variations to the phenotype of drug response'. Mol Psychiatry. 2004, 9: 442-473. 10.1038\u002Fsj.mp.4001494.\nPirmohamed M, Park BK: 'Genetic susceptibility to adverse drug reactions'. Trends Pharmacol Sci. 2001, 22: 298-305. 10.1016\u002FS0165-6147(00)01717-X.\nIngelman-Sundberg M, Oscarson M, McLellan RA: 'Polymorphic human cytochrome P450 enzymes: An opportunity for individualized drug treatment'. Trends Pharmacol Sci. 1999, 20: 342-349. 10.1016\u002FS0165-6147(99)01363-2.\nShows TB, McAlpine PJ, Boucheix C, Collins FS, et al: 'Guidelines for human gene nomenclature. An international system for human gene nomenclature (ISGN, 1987)'. Cytogenet Cell Genet. 1987, 46: 11-28.\nDaly AK, Brockmoller J, Broly F, Eichelbaum M, et al: 'Nomenclature for human CYP2D6 alleles'. Pharmacogenetics. 1996, 6: 193-201. 10.1097\u002F00008571-199606000-00001.\nAntonarakis SE: 'Recommendations for a nomenclature system for human gene mutations. Nomenclature Working Group'. Hum Mutat. 1998, 11: 1-3.\nden Dunnen JT, Antonarakis SE: 'Nomenclature for the description of human sequence variations'. Hum Genet. 2001, 109: 121-124. 10.1007\u002Fs004390100505.\nNelson DR, Zeldin DC, Hoffman SM, Maltais LJ, et al: 'Comparison of cytochrome P450 (CYP) genes from the mouse and human genomes, including nomenclature recommendations for genes, pseudogenes and alternative-splice variants'. Pharmacogenetics. 2004, 14: 1-18. 10.1097\u002F00008571-200401000-00001.\nSim SC, Miller WL, Zhong XB, Arlt W, et al: 'Nomenclature for alleles of the cytochrome P450 oxidoreductase gene'. Pharmacogenet Genomics. 2009, 19: 565-566. 10.1097\u002FFPC.0b013e32832af5b7.",{"EN":756},"Pharmacogenetics affects both pharmacokinetics and pharmacodynamics, thereby influencing an individual's response to drugs, both in terms of response and adverse reactions. Within the area of pharmacogenetics, findings of genetic variation influencing drug levels have been more prevalent, and variation in the cytochrome P450 (CYP) enzymes is one of the most common causes. Much of the work concerning sequence variations in CYPs aims at finding biomarkers of use for individualised treatment, thereby increasing the treatment response, lowering the number of side effects and decreasing the overall cost of treatment regimens. For over ten years, the Human Cytochrome P450 Allele Nomenclature (CYP-allele) website (\n                  http:\u002F\u002Fwww.cypalleles.ki.se\u002F\n                  \n                ) has offered a database of genetic information on CYP variants, along with effects at the molecular as well as clinical level. Thus, this database serves as an assembly of past, current and soon-to-be published information on CYP alleles and their outcome effects. The website is used by academic researchers and companies (eg as a tool in drug development and for outlining new research projects). By providing peer-reviewed genetic information on CYP enzymes, the CYP-allele website has become increasingly popular and widely used. Recently, NADPH cytochrome P450 oxidoreductase (POR), the electron donor for CYP enzymes, was included on the website, which already contains 29 CYP genes, hence POR alleles are now also designated using the star allele (POR*) nomenclature. Although most CYPs on the CYP-allele website are involved in the metabolism of xenobiotics, polymorphic enzymes with endogenous functions are also included. Each gene on the CYP-allele website has its own webpage that lists the different alleles with their nucleotide changes, their functional consequences and links to publications in which the allele has been identified and\u002For characterised. Thus, the CYP-allele website offers a rapid online publication of new alleles, as well as providing an overview of peer-reviewed data.",{"EN":758},"The Human Cytochrome P450 (CYP) Allele Nomenclature website: a peer-reviewed database of CYP variants and their associated effects",{"VOID":760},"10.1186\u002F1479-7364-4-4-278","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1479-7364-4-4-278",[763,778],{"id":764,"sortIndex":135,"researcher":18,"roles":765,"affiliations":766,"properties":775},"452ee7ad-f441-4539-af0f-af24f735f5ab",[433],[767],{"id":18,"sortIndex":19,"affiliation":768,"properties":18},{"id":769,"createTime":770,"updateTime":770,"relativeEntities":771,"slug":18,"properties":772,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"cfebb6d4-6301-449b-9400-c8256d0c627a","2024-02-06T21:15:51.996+00:00",[],{"title":773},{"VI":774},"Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden",{"title":776},{"VI":777},"Magnus Ingelman-Sundberg",{"id":779,"sortIndex":19,"researcher":18,"roles":780,"affiliations":781,"properties":787},"972e97bf-5b85-4a2a-b35d-ccc298c86f18",[433],[782],{"id":18,"sortIndex":19,"affiliation":783,"properties":18},{"id":769,"createTime":770,"updateTime":770,"relativeEntities":784,"slug":18,"properties":785,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":786},{"VI":774},{"title":788},{"VI":789},"Sarah C Sim",{"url":761,"publisher":791,"properties":818},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":792,"slug":10,"properties":793,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":796,"manageAffiliations":797,"indexDatabases":798,"url":18,"thumbnailPath":18,"statistic":813,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":794,"title":795},{"VOID":13},{"EN":15},[],[],[799,806],{"id":97,"indexDatabase":800,"url":110,"indexYears":111,"academicFieldIds":805,"indexDatabaseRanking":117},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":801,"label":802,"description":803,"key":107,"publicationTags":804,"standard":18},[],{"EN":104,"VI":104},{"EN":104,"VI":106},[109],[113,114,115,116],{"id":78,"indexDatabase":807,"url":93,"indexYears":18,"academicFieldIds":812,"indexDatabaseRanking":18},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":808,"label":809,"description":810,"key":89,"publicationTags":811,"standard":18},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95],{"impactFactor":19,"impactFactorByYear":814,"i10Index":132,"i10IndexLast5Year":64,"totalPublication":133,"totalPublicationByYear":815,"totalCitation":140,"totalCitationByYear":816,"totalCitationPerPublication":156,"totalCitationPerPublicationByYear":817,"hindexLast5Year":171,"hindex":171},{"2012":120,"2013":121,"2014":122,"2015":123,"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":130,"2023":131},{"2003":135,"2004":136,"2005":135,"2006":121,"2008":135,"2009":121,"2010":121,"2011":135,"2012":121,"2013":137,"2014":136,"2015":136,"2016":130,"2017":121,"2018":138,"2019":139,"2020":138,"2021":138,"2022":136},{"2003":142,"2004":143,"2006":144,"2008":135,"2009":145,"2010":146,"2011":147,"2012":133,"2013":148,"2014":139,"2015":149,"2016":150,"2017":132,"2018":151,"2019":152,"2020":153,"2021":154,"2022":155},{"2003":142,"2004":158,"2006":159,"2008":135,"2009":160,"2010":161,"2011":147,"2012":162,"2013":163,"2014":164,"2015":139,"2016":165,"2017":166,"2018":167,"2019":168,"2020":169,"2021":166,"2022":170},{"volume":819,"pages":821},{"VOID":820},"4",{"VOID":822},"1-4","2010-04-01",2010,{"id":826,"createTime":827,"updateTime":827,"relativeEntities":828,"slug":18,"properties":829,"entityType":197,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":838,"fullTextUrl":18,"authors":839,"publicationType":295,"publisherRelationship":938,"citationCount":18,"citationInfo":18,"publishDate":971,"publishYear":972,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":412},"96ff4c12-19bf-4a88-be5a-e29b71ec3b12","2023-12-06T23:48:02.085+00:00",[],{"references":830,"abstract":832,"title":834,"doi":836},{"VOID":831},"Osborne JP, Fryer A, Webb D. Epidemiology of tuberous sclerosis. Ann New York Acad Sci. 1991;615(1 Tuberous Scle):125–7. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1749-6632.1991.tb37754.x.\nNorthrup H, Krueger DA, International Tuberous Sclerosis Complex Consensus G. Tuberous sclerosis complex diagnostic criteria update: recommendations of the 2012 International Tuberous Sclerosis Complex Consensus Conference. Pediatr Neurol. 2013;49:243–54.\nKapoor A, Girard L, Lattouf JB, Pei Y, Rendon R, Card P, et al. Evolving strategies in the treatment of tuberous sclerosis complex-associated angiomyolipomas (TSC-AML). Urology. 2016;89:19–26. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.urology.2015.12.009.\nCai Y, Li H, Zhang Y. Assessment of tuberous sclerosis complex associated with renal lesions by targeted next-generation sequencing in mainland China. Urology. 2017;101:170 e1–7.\nZonnenberg BA, Neary MP, Duh MS, Ionescu-Ittu R, Fortier J, Vekeman F. Observational study of characteristics and clinical outcomes of Dutch patients with tuberous sclerosis complex and renal angiomyolipoma treated with everolimus. Plos one. 2018;13(11):e0204646. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0204646.\nCai Y, Guo H, Wang W, Li H, Sun H, Shi B, et al. Assessing the outcomes of everolimus on renal angiomyolipoma associated with tuberous sclerosis complex in China: a two years trial. Orphanet J Rare Dis. 2018;13(1):43. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13023-018-0781-y.\nTrindade AJ, Medvetz DA, Neuman NA, Myachina F, Yu J, Priolo C, et al. MicroRNA-21 is induced by rapamycin in a model of tuberous sclerosis (TSC) and lymphangioleiomyomatosis (LAM). Plos One. 2013;8(3):e60014. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0060014.\nYu R, Yao J, Ren Y. A novel circRNA, circNUP98, a potential biomarker, acted as an oncogene via the miR-567\u002FPRDX3 axis in renal cell carcinoma. 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Am J Physiol Lung Cell Mol Physiol. 2015;309(12):L1447–54. https:\u002F\u002Fdoi.org\u002F10.1152\u002Fajplung.00262.2015.\nKingswood JC, Belousova E, Benedik MP, Carter T, Cottin V, Curatolo P, et al. Renal angiomyolipoma in patients with tuberous sclerosis complex: findings from the TuberOus SClerosis registry to increase disease Awareness. Nephrol Dial Transplant. 2019;34(3):502–8. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fndt\u002Fgfy063.",{"EN":833},"Tuberous sclerosis complex (TSC) is a rare autosomal dominant disease characterized by lesions throughout the body. Our previous study showed the abnormal up-regulation of miRNAs plays an important part in the pathogenesis of TSC-related renal angiomyolipoma (TSC-RAML). circRNAs were known as important regulators of miRNA, but little is known about the circRNAs in TSC-RAMLs. Microarray chips and RNA sequencing were used to identify the circRNAs and mRNAs that were differently expressed between the TSC-RAML and normal kidney tissue. A competitive endogenous RNA (ceRNA) regulatory network was constructed to reveal the regulation of miRNAs and mRNAs by the circRNAs. The biological functions of circRNA and mRNA were analyzed by pathway analysis. Microenvironmental cell types were estimated with the MCP-counter package. We identified 491 differentially expressed circRNAs (DECs) and 212 differentially expressed genes (DEGs), and 6 DECs were further confirmed by q-PCR. A ceRNA regulatory network which included 6 DECs, 5 miRNAs, and 63 mRNAs was established. Lipid biosynthetic process was significantly up-regulated in TSC-RAML, and the humoral immune response and the leukocyte chemotaxis pathway were found to be down-regulated. Fibroblasts are enriched in TSC-RAML, and the up-regulation of circRNA_000799 and circRNA_025332 may be significantly correlated to the infiltration of the fibroblasts. circRNAs may regulate the lipid metabolism of TSC-RAML by regulation of the miRNAs. Fibroblasts are enriched in TSC-RAMLs, and the population of fibroblast may be related to the alteration of circRNAs of TSC-RAML. Lipid metabolism in fibroblasts is a potential treatment target for TSC-RAML.",{"EN":835},"High-throughput screening of circRNAs reveals novel mechanisms of tuberous sclerosis complex-related renal angiomyolipoma",{"VOID":837},"10.1186\u002Fs40246-021-00344-1","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40246-021-00344-1",[840,856,868,880,892,904,916],{"id":841,"sortIndex":136,"researcher":18,"roles":842,"affiliations":843,"properties":853},"f899c3fc-c9e8-4729-9565-522220bc8587",[433],[844],{"id":18,"sortIndex":19,"affiliation":845,"properties":18},{"id":846,"createTime":847,"updateTime":847,"relativeEntities":848,"slug":849,"properties":850,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"e7bbca1a-3465-4a29-90b0-d0325f770032","2024-04-12T04:12:31.698+00:00",[],"Department-of-Urology-Peking-Union-Medical-College-Hospital-Chinese-Academy-of-Medical-Sciences-and-Peking-Union-Medical-College-Beijing-China",{"title":851},{"EN":852},"Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China",{"title":854},{"VI":855},"Guoyang Zheng",{"id":857,"sortIndex":130,"researcher":18,"roles":858,"affiliations":859,"properties":865},"88062369-0eb0-4e73-af52-008110c9840b",[433],[860],{"id":18,"sortIndex":19,"affiliation":861,"properties":18},{"id":846,"createTime":847,"updateTime":847,"relativeEntities":862,"slug":849,"properties":863,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":864},{"EN":852},{"title":866},{"VI":867},"Yushi 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Wang",{"id":893,"sortIndex":206,"researcher":18,"roles":894,"affiliations":895,"properties":901},"77542593-573d-4d52-8ea2-10d39bb267f8",[433],[896],{"id":18,"sortIndex":19,"affiliation":897,"properties":18},{"id":846,"createTime":847,"updateTime":847,"relativeEntities":898,"slug":849,"properties":899,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":900},{"EN":852},{"title":902},{"VI":903},"Zhan Wang",{"id":905,"sortIndex":137,"researcher":18,"roles":906,"affiliations":907,"properties":913},"9c4d0a03-974c-467b-af6d-69768cf40ad7",[433],[908],{"id":18,"sortIndex":19,"affiliation":909,"properties":18},{"id":846,"createTime":847,"updateTime":847,"relativeEntities":910,"slug":849,"properties":911,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":912},{"EN":852},{"title":914},{"VI":915},"Xu 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Journal of Bone and Mineral Research. 2017;32(9):1811–5.\nKozawa O, Hatakeyama D, Uematsu T. Divergent regulation by p44\u002Fp42 MAP kinase and p38 MAP kinase of bone morphogenetic protein-4-stimulated osteocalcin synthesis in osteoblasts. Journal of cellular biochemistry. 2002;84(3):583–9.\nFranceschi RT, Ge C. Control of the osteoblast lineage by mitogen-activated protein kinase signaling. Current molecular biology reports. 2017;3(2):122–32.\nHamidouche Z, Fromigué O, Ringe J, Häupl T, Vaudin P, Pagès J-C, et al. Priming integrin α5 promotes human mesenchymal stromal cell osteoblast differentiation and osteogenesis. Proceedings of the National Academy of Sciences. 2009;106(44):18587–91.\nHamidouche Z, Fromigué O, Ringe J, Häupl T, Marie PJ. Crosstalks between integrin alpha 5 and IGF2\u002FIGFBP2 signalling trigger human bone marrow-derived mesenchymal stromal osteogenic differentiation. BMC cell biology. 2010;11(1):44.\nTucker GC. Integrins: molecular targets in cancer therapy. Current oncology reports. 2006;8(2):96–103.\nLogan NJ, Camman M, Williams G, Higgins CA. Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model. Bone. 2018;117:149–60.\nChen Q, Shou P, Zhang L, Xu C, Zheng C, Han Y, et al. An osteopontin-integrin interaction plays a critical role in directing adipogenesis and osteogenesis by mesenchymal stem cells. Stem cells. 2014;32(2):327–37.\nRamazzotti G, Ratti S, Fiume R, Yung Follo M, Billi AM, Rusciano I, et al. Phosphoinositide 3 kinase signaling in human stem cells from reprogramming to differentiation: a tale in cytoplasmic and nuclear compartments. International journal of molecular sciences. 2019;20(8):2026.\nDi Benedetto A, Watkins M, Grimston S, Salazar V, Donsante C, Mbalaviele G, et al. N-cadherin and cadherin 11 modulate postnatal bone growth and osteoblast differentiation by distinct mechanisms. J Cell Sci. 2010;123(15):2640–8.\nShen B, Vardy K, Hughes P, Tasdogan A, Zhao Z, Yue R, et al. Integrin alpha11 is an Osteolectin receptor and is required for the maintenance of adult skeletal bone mass. Elife. 2019;8:e42274.\nZhang W, Chen E, Chen M, Ye C, Qi Y, Ding Q, et al. IGFBP7 regulates the osteogenic differentiation of bone marrow–derived mesenchymal stem cells via Wnt\u002Fβ-catenin signaling pathway. The FASEB Journal. 2018;32(4):2280–91.\nMalysheva K. Rooij Kd, WGM Löwik C, L Baeten D, Rose-John S. Interleukin 6\u002FWnt interactions in rheumatoid arthritis: interleukin 6 inhibits Wnt signaling in synovial fibroblasts and osteoblasts. Croatian medical journal. 2016;57(2):89–98.\nLi X, Zhou Z-y, Zhang Y-y, Yang H-l. IL-6 contributes to the defective osteogenesis of bone marrow stromal cells from the vertebral body of the glucocorticoid-induced osteoporotic mouse. PLoS One. 2016;11(4):e0154677.\nLanghammer T-S, Roolf C, Krohn S, Kretzschmar C, Huebner R, Rolfs A, et al. PI3K\u002FAkt signaling interacts with Wnt\u002Fβ-Catenin signaling but does not induce an accumulation of β-catenin in the nucleus of acute lymphoblastic leukemia cell lines. DC: American Society of Hematology Washington; 2013.\nHan L, Yang Y, Yue X, Huang K, Liu X, Pu P, et al. Inactivation of PI3K\u002FAKT signaling inhibits glioma cell growth through modulation of β-catenin-mediated transcription. Brain research. 2010;1366:9–17.\nFang D, Hawke D, Zheng Y, Xia Y, Meisenhelder J, Nika H, et al. Phosphorylation of β-catenin by AKT promotes β-catenin transcriptional activity. Journal of Biological Chemistry. 2007;282(15):11221–9.\nDuan X, Murata Y, Liu Y, Nicolae C, Olsen BR, Berendsen AD. Vegfa regulates perichondrial vascularity and osteoblast differentiation in bone development. Development. 2015;142(11):1984–91.\nGrosso A, Burger MG, Lunger A, Schaefer DJ, Banfi A, Di Maggio N. It takes two to tango: coupling of angiogenesis and osteogenesis for bone regeneration. Frontiers in bioengineering and biotechnology. 2017;5:68.\nMaes C, Goossens S, Bartunkova S, Drogat B, Coenegrachts L, Stockmans I, et al. Increased skeletal VEGF enhances β-catenin activity and results in excessively ossified bones. The EMBO journal. 2010;29(2):424–41.\nOlsen JJ, Pohl SÖ-G, Deshmukh A, Visweswaran M, Ward NC, Arfuso F, et al. The role of Wnt signalling in angiogenesis. The Clinical Biochemist Reviews. 2017;38(3):131.\nRuijtenberg S, van den Heuvel S. Coordinating cell proliferation and differentiation: Antagonism between cell cycle regulators and cell type-specific gene expression. Cell cycle. 2016;15(2):196–212.\nJiang H, Hong T, Wang T, Wang X, Cao L, Xu X, et al. Gene expression profiling of human bone marrow mesenchymal stem cells during osteogenic differentiation. Journal of cellular physiology. 2019;234(5):7070–7.\nLi L, Zhang C, Chen Jl, Hong Ff, Chen P, Wang Jf. Effects of simulated microgravity on the expression profiles of RNA during osteogenic differentiation of human bone marrow mesenchymal stem cells. Cell proliferation. 2019;52(2):e12539.\nDavidson G, Shen J, Huang Y-L, Su Y, Karaulanov E, Bartscherer K, et al. Cell cycle control of wnt receptor activation. Developmental cell. 2009;17(6):788–99.\nNiehrs C, Acebron SP. Mitotic and mitogenic Wnt signalling. The EMBO journal. 2012;31(12):2705–13.\nČervenka I, Wolf J, Mašek J, Krejci P, Wilcox WR, Kozubík A, et al. Mitogen-activated protein kinases promote WNT\u002Fβ-catenin signaling via phosphorylation of LRP6. Molecular and cellular biology. 2011;31(1):179–89.\nZhang M, Pritchard MR, Middleton FA, Horton JA, Damron TA. Microarray analysis of perichondral and reserve growth plate zones identifies differential gene expressions and signal pathways. Bone. 2008;43(3):511–20.\nWodarz A, Nusse R. Mechanisms of Wnt signaling in development. Annual review of cell and developmental biology. 1998;14(1):59–88.\nHuelsken J, Vogel R, Brinkmann V, Erdmann B, Birchmeier C, Birchmeier W. Requirement for β-catenin in anterior-posterior axis formation in mice. The Journal of cell biology. 2000;148(3):567–78.\nHouschyar KS, Tapking C, Borrelli MR, Popp D, Duscher D, Maan ZN, et al. Wnt pathway in bone repair and regeneration–what do we know so far. Frontiers in cell and developmental biology. 2018;6.\nJames AW. Review of signaling pathways governing MSC osteogenic and adipogenic differentiation. Scientifica. 2013;2013.\nGaur T, Lengner CJ, Hovhannisyan H, Bhat RA, Bodine PV, Komm BS, et al. Canonical WNT signaling promotes osteogenesis by directly stimulating Runx2 gene expression. Journal of Biological Chemistry. 2005;280(39):33132–40.\nTornero-Esteban P, Peralta-Sastre A, Herranz E, Rodríguez-Rodríguez L, Mucientes A, Abásolo L, et al. Altered expression of Wnt signaling pathway components in osteogenesis of mesenchymal stem cells in osteoarthritis patients. PLoS One. 2015;10(9):e0137170.\nNovak A, Hsu S-C, Leung-Hagesteijn C, Radeva G, Papkoff J, Montesano R, et al. Cell adhesion and the integrin-linked kinase regulate the LEF-1 and β-catenin signaling pathways. 1998;95(8):4374-9.\nIshii T, Furuoka H, Muroi Y, Nishimura MJJoBC. Inactivation of integrin-linked kinase induces aberrant tau phosphorylation via sustained activation of glycogen synthase kinase 3β in N1E-115 neuroblastoma cells. 2003;278(29):26970-5.\nSaidak Z, Le Henaff C, Azzi S, Marty C, Da Nascimento S, Sonnet P, et al. Wnt\u002Fβ-catenin signaling mediates osteoblast differentiation triggered by peptide-induced α5β1 integrin priming in mesenchymal skeletal cells. Journal of Biological Chemistry. 2015;290(11):6903–12.\nDesbois-Mouthon C, Cadoret A, Blivet-Van Eggelpoel M-J, Bertrand F, Cherqui G, Perret C, et al. Insulin and IGF-1 stimulate the β-catenin pathway through two signalling cascades involving GSK-3β inhibition and Ras activation. Oncogene. 2001;20(2):252.\nMorali OG, Delmas V, Moore R, Jeanney C, Thiery JP, Larue L. IGF-II induces rapid β-catenin relocation to the nucleus during epithelium to mesenchyme transition. Oncogene. 2001;20(36):4942.\nFelber K, Elks PM, Lecca M, Roehl HH. Expression of osterix is regulated by FGF and Wnt\u002Fβ-catenin signalling during osteoblast differentiation. PloS one. 2015;10(12):e0144982.\nWang Y, Zhang X, Shao J, Liu H, Liu X. Luo EJSr. Adiponectin regulates BMSC osteogenic differentiation and osteogenesis through the Wnt\u002Fβ-catenin pathway. 2017;7(1):1–13.\nAlves RD, Eijken M, van de Peppel J, van Leeuwen JP. Calcifying vascular smooth muscle cells and osteoblasts: independent cell types exhibiting extracellular matrix and biomineralization-related mimicries. BMC genomics. 2014;15(1):1–14.",{"EN":983},"Adult bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stem cells that can differentiate into three lineages. They are suitable sources for cell-based therapy and regenerative medicine applications. This study aims to evaluate the hub genes and key pathways of differentially expressed genes (DEGs) related to osteogenesis by bioinformatics analysis in three different days. The DEGs were derived from the three different days compared with day 0. Gene expression profiles of GSE37558 were obtained from the Gene Expression Omnibus (GEO) database. A total of 4076 DEGs were acquired on days 8, 12, and 25. Gene ontology (GO) enrichment analysis showed that the non-canonical Wnt signaling pathway and lipopolysaccharide (LPS)-mediated signaling pathway were commonly upregulated DEGs for all 3 days. KEGG pathway analysis indicated that the PI3K-Akt and focal adhesion were also commonly upregulated DEGs for all 3 days. Ten hub genes were identified by CytoHubba on days 8, 12, and 25. Then, we focused on the association of these hub genes with the Wnt pathways that had been enriched from the protein-protein interaction (PPI) by the Cytoscape plugin MCODE. These findings suggested further insights into the roles of the PI3K\u002FAKT and Wnt pathways and their association with osteogenesis. In addition, the stem cell microenvironment via growth factors, extracellular matrix (ECM), IGF1, IGF2, LPS, and Wnt most likely affect osteogenesis by PI3K\u002FAKT.",{"EN":985},"Microarray analysis identification of key pathways and interaction network of differential gene expressions during osteogenic differentiation",{"VOID":987},"10.1186\u002Fs40246-020-00293-1","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40246-020-00293-1",[990,1007,1022,1034,1049,1061,1076],{"id":991,"sortIndex":130,"researcher":18,"roles":992,"affiliations":993,"properties":1004},"94395cc1-b0c2-4811-a3da-e1d45bad0b4c",[433],[994],{"id":18,"sortIndex":19,"affiliation":995,"properties":18},{"id":996,"createTime":997,"updateTime":998,"relativeEntities":999,"slug":1000,"properties":1001,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"7eab77f2-9146-4914-a50b-9492dd68904b","2023-12-05T12:12:09.792+00:00","2024-10-04T06:54:27.452+00:00",[],"Department-of-Stem-Cells-and-Developmental-Biology-Cell-Science-Research-Center-Royan-Institute-for-Stem-Cell-Biology-and-Technology-ACECR-Tehran-Iran",{"title":1002},{"VI":1003},"Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran",{"title":1005},{"VI":1006},"Mohamadreza Baghaban Eslaminejad",{"id":1008,"sortIndex":121,"researcher":18,"roles":1009,"affiliations":1010,"properties":1019},"6a5ade6e-de57-4501-8300-0a9baa90158e",[433],[1011],{"id":18,"sortIndex":19,"affiliation":1012,"properties":18},{"id":1013,"createTime":1014,"updateTime":1014,"relativeEntities":1015,"slug":18,"properties":1016,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1d66f0c6-e64d-4208-9f03-11b0029264b5","2023-12-26T12:01:23.626+00:00",[],{"title":1017},{"VI":1018},"Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran",{"title":1020},{"VI":1021},"Reza Aflatoonian",{"id":1023,"sortIndex":135,"researcher":18,"roles":1024,"affiliations":1025,"properties":1031},"b2b2d13a-df85-46b6-b55e-6fce139de71d",[433],[1026],{"id":18,"sortIndex":19,"affiliation":1027,"properties":18},{"id":996,"createTime":997,"updateTime":998,"relativeEntities":1028,"slug":1000,"properties":1029,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1030},{"VI":1003},{"title":1032},{"VI":1033},"Sara Taleahmad",{"id":1035,"sortIndex":137,"researcher":18,"roles":1036,"affiliations":1037,"properties":1046},"2ed4c837-fc6b-499f-97e5-b5f8a3cdbf13",[433],[1038],{"id":18,"sortIndex":19,"affiliation":1039,"properties":18},{"id":1040,"createTime":1041,"updateTime":1041,"relativeEntities":1042,"slug":18,"properties":1043,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1cebfb95-d598-4b2c-ae5a-1e3ef4caff63","2023-12-19T23:57:54.678+00:00",[],{"title":1044},{"VI":1045},"Cellular and Molecular Research Center, Research Institute for prevention of Non-Communicable Disease, Qazvin University of Medical Sciences, Qazvin, Iran",{"title":1047},{"VI":1048},"Marjan Nassiri-Asl",{"id":1050,"sortIndex":136,"researcher":18,"roles":1051,"affiliations":1052,"properties":1058},"c8defb8f-3801-41c4-b2ba-1ea036d6810f",[433],[1053],{"id":18,"sortIndex":19,"affiliation":1054,"properties":18},{"id":1040,"createTime":1041,"updateTime":1041,"relativeEntities":1055,"slug":18,"properties":1056,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1057},{"VI":1045},{"title":1059},{"VI":1060},"Farzad Rajaei",{"id":1062,"sortIndex":19,"researcher":18,"roles":1063,"affiliations":1064,"properties":1073},"f39c0c4e-11e2-44df-8fa5-67b0b65f0d89",[433],[1065],{"id":18,"sortIndex":19,"affiliation":1066,"properties":18},{"id":1067,"createTime":1068,"updateTime":1068,"relativeEntities":1069,"slug":18,"properties":1070,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"4f9c9c03-8bed-4933-b107-f5b4b5a63f92","2023-12-28T08:24:08.549+00:00",[],{"title":1071},{"VI":1072},"Department of Molecular Medicine, Qazvin University of Medical Sciences, Qazvin, Iran",{"title":1074},{"VI":1075},"Fatemeh Khodabandehloo",{"id":1077,"sortIndex":206,"researcher":18,"roles":1078,"affiliations":1079,"properties":1088},"93da99af-f4f1-4c22-a246-e23d0fcdb8b6",[433],[1080],{"id":18,"sortIndex":19,"affiliation":1081,"properties":18},{"id":1082,"createTime":1083,"updateTime":1083,"relativeEntities":1084,"slug":18,"properties":1085,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"20c190c2-c03c-43ec-8145-ed29b87e5f45","2023-12-07T00:29:06.684+00:00",[],{"title":1086},{"VI":1087},"Department of Anatomy, School of Medicine, Iran University of Medical Sciences, Tehran, Iran",{"title":1089},{"VI":1090},"Zahra Zandieh",{"url":988,"publisher":1092,"properties":1119},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1093,"slug":10,"properties":1094,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1097,"manageAffiliations":1098,"indexDatabases":1099,"url":18,"thumbnailPath":18,"statistic":1114,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":1095,"title":1096},{"VOID":13},{"EN":15},[],[],[1100,1107],{"id":97,"indexDatabase":1101,"url":110,"indexYears":111,"academicFieldIds":1106,"indexDatabaseRanking":117},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":1102,"label":1103,"description":1104,"key":107,"publicationTags":1105,"standard":18},[],{"EN":104,"VI":104},{"EN":104,"VI":106},[109],[113,114,115,116],{"id":78,"indexDatabase":1108,"url":93,"indexYears":18,"academicFieldIds":1113,"indexDatabaseRanking":18},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":1109,"label":1110,"description":1111,"key":89,"publicationTags":1112,"standard":18},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95],{"impactFactor":19,"impactFactorByYear":1115,"i10Index":132,"i10IndexLast5Year":64,"totalPublication":133,"totalPublicationByYear":1116,"totalCitation":140,"totalCitationByYear":1117,"totalCitationPerPublication":156,"totalCitationPerPublicationByYear":1118,"hindexLast5Year":171,"hindex":171},{"2012":120,"2013":121,"2014":122,"2015":123,"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":130,"2023":131},{"2003":135,"2004":136,"2005":135,"2006":121,"2008":135,"2009":121,"2010":121,"2011":135,"2012":121,"2013":137,"2014":136,"2015":136,"2016":130,"2017":121,"2018":138,"2019":139,"2020":138,"2021":138,"2022":136},{"2003":142,"2004":143,"2006":144,"2008":135,"2009":145,"2010":146,"2011":147,"2012":133,"2013":148,"2014":139,"2015":149,"2016":150,"2017":132,"2018":151,"2019":152,"2020":153,"2021":154,"2022":155},{"2003":142,"2004":158,"2006":159,"2008":135,"2009":160,"2010":161,"2011":147,"2012":162,"2013":163,"2014":164,"2015":139,"2016":165,"2017":166,"2018":167,"2019":168,"2020":169,"2021":166,"2022":170},{"volume":1120,"pages":1122},{"VOID":1121},"14",{"VOID":1123},"1-13","2020-11-25",2020,{"id":1127,"createTime":1128,"updateTime":1129,"relativeEntities":1130,"slug":1131,"properties":1132,"entityType":197,"verifyStatus":198,"verifyTime":1129,"verifyNote":199,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1141,"fullTextUrl":18,"authors":1142,"publicationType":295,"publisherRelationship":1379,"citationCount":18,"citationInfo":18,"publishDate":1411,"publishYear":1412,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":412},"805ef90d-3b9b-4386-adff-ea6bcba46e3c","2024-02-22T04:33:40.053+00:00","2025-02-14T23:41:17.010+00:00",[],"Mutation-screening-of-SPTLC1-and-SPTLC2-in-amyotrophic-lateral-sclerosis",{"references":1133,"abstract":1135,"title":1137,"doi":1139},{"VOID":1134},"Hardiman O, Al-Chalabi A, Chio A, Corr EM, Logroscino G, Robberecht W, Shaw PJ, Simmons Z, van den Berg LH. Amyotrophic lateral sclerosis. Nat Rev Dis Primers. 2017;3(1):17071.\nGhasemi M, Brown RH Jr. Genetics of amyotrophic lateral sclerosis. Cold Spring Harbor Perspect Med. 2018;8(5):a024125.\nJohnson JO, Chia R, Miller DE, Li R, Kumaran R, Abramzon Y, Alahmady N, Renton AE, Topp SD, Gibbs JR, et al. Association of variants in the SPTLC1 gene with juvenile amyotrophic lateral sclerosis. JAMA Neurol. 2021;78(10):1236–48.\nMohassel P, Donkervoort S, Lone MA, Nalls M, Gable K, Gupta SD, Foley AR, Hu Y, Saute JAM, Moreira AL, et al. Childhood amyotrophic lateral sclerosis caused by excess sphingolipid synthesis. Nat Med. 2021;27(7):1197–204.\nRotthier A, Auer-Grumbach M, Janssens K, Baets J, Penno A, Almeida-Souza L, Van Hoof K, Jacobs A, De Vriendt E, Schlotter-Weigel B, et al. Mutations in the SPTLC2 subunit of serine palmitoyltransferase cause hereditary sensory and autonomic neuropathy type I. Am J Hum Genet. 2010;87(4):513–22.\nRees E, Kirov G, Walters JT, Richards AL, Howrigan D, Kavanagh DH, Pocklington AJ, Fromer M, Ruderfer DM, Georgieva L, et al. Analysis of exome sequence in 604 trios for recessive genotypes in schizophrenia. Transl Psychiatr. 2015;5(7): e607.\nLi C, Ou R, Chen Y, Gu X, Wei Q, Cao B, Zhang L, Hou Y, Liu K, Chen X, et al. Mutation analysis of DNAJC family for early-onset Parkinson’s Disease in a Chinese cohort. Mov Disord Off J Mov Disord Soc. 2020;35(11):2068–76.\nBode H, Bourquin F, Suriyanarayanan S, Wei Y, Alecu I, Othman A, Von Eckardstein A, Hornemann T. HSAN1 mutations in serine palmitoyltransferase reveal a close structure–function–phenotype relationship. Hum Mol Genet. 2016;25(5):853–65.\nWu J, Ma S, Sandhoff R, Ming Y, Hotz-Wagenblatt A, Timmerman V, Bonello-Palot N, Schlotter-Weigel B, Auer-Grumbach M, Seeman P, et al. Loss of neurological disease HSAN-I-associated gene SPTLC2 impairs CD8(+) T cell responses to infection by inhibiting T cell metabolic fitness. Immunity. 2019;50(5):1218-1231.e1215.",{"EN":1136},"Recently, several rare variants of SPTLC1 were identified as disease cause for juvenile amyotrophic lateral sclerosis (ALS) by disrupting the normal homeostatic regulation of serine palmitoyltransferase (SPT). However, further exploration of the rare variants in large cohorts was still necessary. Meanwhile, SPTLC2 plays a similar role as SPTLC1 in the SPT function. To explore the genetic role of SPTLC1 and SPTLC2 in ALS, we analyzed the rare protein-coding variants in 2011 patients with ALS and 3298 controls from the Chinese population with whole exome sequencing. Fisher’s exact test was performed between each variant and disease risk, while at gene level over-representation of rare variants in patients was examined with optimized sequence kernel association test (SKAT-O). Totally 33 rare variants with minor allele frequency \u003C 0.01 were identified, including 17 in SPTLC1 and 16 in SPTLC2. One adult-onset patient carried the variant p.E406K (SPTLC1) which was reported in previous study. Additionally, three adult-onset patients carried variants in the same amino acids as the variants identified in previous studies (p.Y509C, p.S331T, and p.R239Q in SPTLC1). At gene level, rare variants of SPTLC1 and STPLC2 were not enriched in patients. These results broadened the variant spectrum of SPTLC1 and SPTLC2 in ALS, and paved the way for future research. Further replication was still needed to explore the genetic role of SPTLC1 in ALS.",{"EN":1138},"Mutation screening of SPTLC1 and SPTLC2 in amyotrophic lateral sclerosis",{"VOID":1140},"10.1186\u002Fs40246-023-00479-3","https:\u002F\u002Fhumgenomics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40246-023-00479-3",[1143,1159,1172,1184,1196,1208,1220,1232,1244,1256,1268,1281,1294,1306,1319,1331,1343,1355,1367],{"id":1144,"sortIndex":1145,"researcher":18,"roles":1146,"affiliations":1147,"properties":1156},"8cd19f3c-de42-4015-8363-3312ea06f499",16,[433],[1148],{"id":18,"sortIndex":19,"affiliation":1149,"properties":18},{"id":1150,"createTime":1151,"updateTime":1151,"relativeEntities":1152,"slug":18,"properties":1153,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"d2106dca-7fb7-40fc-a758-99c61659fd60","2024-02-22T04:33:40.148+00:00",[],{"title":1154},{"VI":1155},"Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China",{"title":1157},{"VI":1158},"Bei Cao",{"id":1160,"sortIndex":1161,"researcher":18,"roles":1162,"affiliations":1163,"properties":1169},"5a950bbc-c69a-46b2-9d3f-e0678019c6e4",18,[433],[1164],{"id":18,"sortIndex":19,"affiliation":1165,"properties":18},{"id":1150,"createTime":1151,"updateTime":1151,"relativeEntities":1166,"slug":18,"properties":1167,"entityType":63,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1168},{"VI":1155},{"title":1170},{"VI":1171},"Huifang 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J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H. Feature selection: A data perspective. arXiv preprint arXiv:1601.07996. 2016.\nChen C, Grennan K, Badner J, Zhang D, Gershon E, Jin L, Liu C. Removing batch effects in analysis of expression microarray data: An evaluation of six batch adjustment methods. PLoS ONE. 2011; 6(2):17238.\nAlmeida A, Paul JT, Magdelenat H, Radvanyi F. Gene expression analysis by real-time reverse transcription polymerase chain reaction: influence of tissue handling. Anal Biochem. 2004; 328(2):101–8.\nMa Y, Dai H, Kong X. Impact of warm ischemia on gene expression analysis in surgically removed biosamples. Anal Biochem. 2012; 423(2):229–35.\nBakay M, Chen YW, Borup R, Zhao P, Nagaraju K, Hoffman EP. Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics. 2002; 3(1):4.\nBoedigheimer MJ, et al.Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics. 2008; 9(1):285.\nFare TL, et al.Effects of atmospheric ozone on microarray data quality. Anal Chem. 2003; 75(17):4672–5.\nGlaab E, Schneider R. Repexplore: addressing technical replicate variance in proteomics and metabolomics data analysis. Bioinformatics. 2015; 31(13):2235–7.\nZhao Z, Liu H. Multi-source feature selection via geometry-dependent covariance analysis. JMLR Work Conf Proc. 2008; 4:36–47.\nTang J, Hu X, Gao H, Liu H. Unsupervised feature selection for multi-view data in social media. In: Proceedings of the 2013 SIAM International Conference on Data Mining.2013. p. 270–8.\nFeng Y, Xiao J, Zhuang Y, Liu X. Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Computer Vision–ACCV 2012. Berlin: Springer: 2013. p. 343–57.\nWang H, Nie F, Huang H. Multi-view clustering and feature learning via structured sparsity. In Proceedings of the 30th International Conference on Machine Learning. 2013:352–60.\nFriedman J, Hastie T, Tibshirani R. A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736. 2010.\nPeng J, et al.Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Ann Appl Stat. 2010; 4(1):53.\nd’Aspremont A, Ghaoui LE, Jordan MI, Lanckriet GR. A direct formulation for sparse pca using semidefinite programming. SIAM Rev. 2007; 49(3):434–48.\nLu M, Huang JZ, Qian X. Sparse exponential family principal component analysis. Pattern Recog. 2016; 60:681–91.\nLeek JT, Store JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007; 3:161.\nGagnon-Bartsch JA, Speed TP. Using control genes to correct for unwanted variation in microarray data. Biostatistics. 2012; 13(3):539–52.\nLu M. An embedded method for gene identification in heterogenous data involving unwanted heterogeneity. In Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine. 2018:242–7.\nTrevor H, Robert T, Andreas B. Flexible discriminant analysis by optimal scoring. J Am Stat Assoc. 1994; 89(428):1255–70.\nDavid W, Srikantan N. Iterative reweighted l1 and l2 methods for finding sparse solutions. IEEE J Sel Top Sign Process. 2010; 4(2):317–29.\nCope LM, Irizarry RA, Jaffee HA, Wu Z, Speed TP. A benchmark for affymetrix genechip expression measures. Bioinformatics. 2004; 20(3):323–31.\nIrizarry RA, et al.Summaries of affymetrix genechip probe level data. Nucleic Acids Res. 2003; 31(4):e15.\nIrizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003; 4:249–64.\nVawter MP, et al.Gender-specific gene expression in post-mortem human brain: Localization to sex chromosomes. Neuropsychopharmacology. 2004; 29(2):373–84.\nEisenberg E, Levanon EY. Human housekeeping genes are compact. TRENDS Genet. 2003; 19(7):362–5.\nBaird S, Fitch D, Kassem I, Emmons S. Pattern formation in the nematode epidermis: determination of the arrangement of peripheral sense organs in the c.elegans male tail. Development. 1991; 113:515–26.\nTan J, et al.Integrative epigenome analysis identifies a polycomb-targeted differentiation program as a tumor-suppressor event epigenetically inactivated in colorectal cancer. Cell Death Dis. 2014; 5(7):1324.\nAgus DB, Bunn PA, Franklin W, et al.Her-2\u002Fneu as a therapeutic target in non-small cell lung cancer, prostate cancer, and ovarian cancer. Semin Oncol. 2000; 27(6):53–63.\nOh JJ, Grosshans DR, Wong SG, et al.Identification of differentially expressed genes associated with her-2\u002Fneu overexpression in human breast cancer cells. Nucleic Acids Res. 1999; 27(20):4008–17.\nPal P, Xi H, Sun G, Kaushal R, Meeks J, Thaxton C, et al.Tagging snps in the kallikrein genes 3 and 2 on 19q13 and their associations with prostate cancer in men of european origin. Hum Genet. 2007; 122:251–9.\nNam R, Zhang W, Trachtenberg J, Diamandis E, Toi A, Emami M, et al.Single nucleotide polymorphism of the human kallikrein-2 gene highly correlates with serum human kallikrein-2 levels and in combination enhances prostate cancer detection. J Clin Oncol. 2003; 21:2312–9.\nZhu C, Feng X, Ye G, Huang T. Meta-analysis of possible role of cadherin gene methylation in evolution and prognosis of hepatocellular carcinoma with a prisma guideline. Med (Baltimore). 2017; 96(16):6650.\nZhang B, Kirov S, Snoddy J. Webgestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005; 33(Web Server issue):741–8.",{"EN":1423},"Modern applications such as bioinformatics collecting data in various ways can easily result in heterogeneous data. Traditional variable selection methods assume samples are independent and identically distributed, which however is not suitable for these applications. Some existing statistical models capable of taking care of unwanted variation were developed for gene identification involving heterogeneous data, but they lack model predictability and suffer from variable redundancy. By accounting for the unwanted heterogeneity effectively, our method have shown its superiority over several state-of-the art methods, which is validated by the experimental results in both unsupervised and supervised gene identification problems. Moreover, we also applied our method to a pan-cancer study where our method can identify the most discriminative genes best distinguishing different cancer types. This article provides an alternative gene identification method that can accounting for unwanted data heterogeneity. 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Non-invasive prenatal chromosomal aneuploidy testing--clinical experience: 100,000 clinical samples. PLoS One. 2014;9(10):e109173.\nHu, H., et al., Clinical experience of non-invasive prenatal chromosomal aneuploidy testing in 190,277 Patient Samples. 2016. 16(8): p. -.\nMccullough, R.M., et al., Non-invasive prenatal chromosomal aneuploidy testing - clinical experience: 100,000 Clinical Samples. 2014. 9(10): p. e109173.\nHu, H., et al., Noninvasive prenatal testing for chromosome aneuploidies and subchromosomal microdeletions\u002Fmicroduplications in a cohort of 8141 single pregnancies. 2019. 13(1): p. 14.\nLiang D, et al. Clinical utility of noninvasive prenatal screening for expanded chromosome disease syndromes. Genet Med. 2019.\nRose NC, Benn P, Milunsky A. Current controversies in prenatal diagnosis 1: should NIPT routinely include microdeletions\u002Fmicroduplications? Prenat Diagn. 2016;36(1):10–4.\nEvans, M.I., et al., Noninvasive prenatal screening or advanced diagnostic testing: caveat emptor. 2016. 215(3): p. 298-305.\nNeofytou MC, et al. Targeted capture enrichment assay for non-invasive prenatal testing of large and small size sub-chromosomal deletions and duplications. PLoS One. 2017;12(2):e0171319.\nYaron Y, et al. Current status of testing for microdeletion syndromes and rare autosomal trisomies using cell-free DNA technology. Obstet Gynecol. 2015;126(5):1095–9.\nMary E, et al. Cell-free DNA analysis for noninvasive examination of trisomy. N Engl J Med. 2015;372:1589–97.\nVerma, I.C., R. Dua-Puri, and S.J.J.o.F.M. Bijarnia-Mahay, ACMG 2016 update on noninvasive prenatal testing for fetal aneuploidy: implications for India. 2017. 4(1): p. 1-6.\nLutgendorf, M.A., et al., Noninvasive prenatal testing: limitations and unanswered questions. 2014. 16(4): p. 281-285.\nSchwartz, S., et al., clinical experience of laboratory follow-up with non-invasive prenatal testing using cell-free DNA and positive microdeletion results in 349 cases. 2018.\nTjoa, M.L., et al., Trophoblastic oxidative stress and the release of cell-free feto-placental DNA. 2006. 169(2): p. 400-404.\nMardy, A. and R.J.J.A.J.o.M.G.P.C.S.i.M.G. Wapner, Confined placental mosaicism and its impact on confirmation of NIPT results. 2016. 172(2): p. 118-122.\nGrati, F.R., et al., Fetoplacental mosaicism: potential implications for false-positive and false-negative noninvasive prenatal screening results. 2014. 16(8): p. 620.\nGregg, A.R., et al., Noninvasive prenatal screening for fetal aneuploidy, 2016 update: a position statement of the American College of Medical Genetics and Genomics. 2016. 18(10): p. 1056-1065.\nNorton ME. et al. Cell-free DNA analysis for noninvasive examination of trisomy. 2015;372(17):1589–97.\nYu, B., et al., Overall evaluation of the clinical value of prenatal screening for fetal-free DNA in maternal blood. 2017. 96(27): p. e7114.\nGirirajan, S., C.D. Campbell, and E.E.J.A.R.o.G. Eichler, Human copy number variation and complex genetic disease. 2011. 45(1): p. 203-226.\nWapner RJ, et al. Chromosomal microarray versus karyotyping for prenatal diagnosis. N Engl J Med. 2012;367(23):2175–84.\nMiller, D.T., et al., Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. 2010. 86(5): p. 749-764.\nMelanie, M. and H.J.G.i.M. Louanne, Array-based technology and recommendations for utilization in medical genetics practice for detection of chromosomal abnormalities. 2010. 12(11): p. 742-745.\nNiederstrasser, S.L., et al., Fetal loss following invasive prenatal testing: a comparison of transabdominal chorionic villus sampling, transcervical chorionic villus sampling and amniocentesis. 2016. 37(S 01).\nTabor A, Alfirevic Z. Update on procedure-related risks for prenatal diagnosis techniques. Fetal Diagn Ther. 2010;27(1):1–7.\nSrinivasan A, et al. Noninvasive detection of fetal subchromosome abnormalities via deep sequencing of maternal plasma. Am J Hum Genet. 2013;92(2):167–76.\nMartin K, et al. Clinical experience with a single-nucleotide polymorphism-based non-invasive prenatal test for five clinically significant microdeletions. Clin Genet. 2018;93(2):293–300.\nNicolaides, K.H., %J American Journal of Obstetrics and Gynecology, Nuchal translucency and other first-trimester sonographic markers of chromosomal abnormalities. 2004. 191(1): p. 45-67.\nChing-Hua, H., et al., Extended first-trimester screening using multiple sonographic markers and maternal serum biochemistry: a five-year prospective study. 2014. 35(4): p. 296-301.\nGonzalez Garcia, J.R. and J.P.J.B. Mezaespinoza, International system for human cytogenetic nomenclature (ISCN). 2006. 108(12): p. 3952.",{"EN":1489},"Since the discovery of cell-free DNA (cfDNA) in maternal plasma, it has opened up new approaches for non-invasive prenatal testing. With the development of whole-genome sequencing, small subchromosomal deletions and duplications could be found by NIPT. This study is to review the efficacy of NIPT as a screening test for aneuploidies and CNVs in 42,910 single pregnancies. A total of 42,910 single pregnancies with different clinical features were recruited. The cell-free fetal DNA was directly sequenced. Each of the chromosome aneuploidies and the subchromosomal microdeletions\u002Fmicroduplications of PPV were analyzed. A total of 534 pregnancies (1.24%) were abnormal results detected by NIPT, and 403 pregnancies had underwent prenatal diagnosis. The positive predictive value (PPV) for trisomy 21(T21), trisomy 18 (T18), trisomy 13 (T13), sex chromosome aneuploidies (SCAs), and other chromosome aneuploidy was 79.23%, 54.84%, 13.79%, 33.04%, and 9.38% respectively. The PPV for CNVs was 28.99%. The PPV for CNVs ≤ 5 Mb is 20.83%, for within 5–10 Mb 50.00%, for > 10 Mb 27.27% respectively. PPVs of NIPT according to pregnancies characteristics are also different. Our data have potential significance in demonstrating the usefulness of NIPT profiling not only for common whole chromosome aneuploidies but also for CNVs. However, this newest method is still in its infancy for CNVs. There is still a need for clinical validation studies with accurate detection rates and false positive rates in clinical practice.",{"EN":1491},"Noninvasive prenatal testing for chromosome aneuploidies and subchromosomal microdeletions\u002Fmicroduplications in a cohort of 42,910 single pregnancies with different clinical 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