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Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.",{"EN":69},"Bayesian estimation of the discrete coefficient of determination",{"VOID":71},"[\"17632429428726253762\"]",{"VOID":73},"S Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor. Biol. 22(3), 437–467 (1969).\nS Kauffman, The Origins of Order: Self-Organization and Selection in Evolution (Oxford University Press, New York, NY, 1993).\nS Bornholdt, Boolean network models of cellular regulation: prospects and limitations. J. R. Soc. Interface. 5(1), S85—S94 (2008).\nR Albert, H Othmer, The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in drosophila melanogaster. J. Theor. Biol. 223(1), 1–18 (2003).\nF Li, YLu T Long, Q Ouyang, C Tang, The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. U.S.A.101(14), 4781–4876 (2004).\nA Faure, A Naldi, C Chaouiya, D Thieffry, Dynamical analysis of a generic boolean model for the control of the mammalian cell cycle. Bionformatics. 22(14), 124–131 (2006).\nER Dougherty, S Kim, Y Chen, Coefficient of determination in nonlinear signal processing. EURASIP J. Signal Process. 80(10), 2219–2235 (2000).\nS Kim, ER Dougherty, Y Chen, K Sivakumar, P Meltzer, JM Trent, M Bittner, Multivariate measurement of gene expression relationships. Genom. 67(2), 201–209 (2000).\nX Zhou, X Wang, ER Dougherty, Binarization of microarray data based on a mixture model. Mol. Cancer Ther. 2(7), 679–684 (2003).\nS Kim, ER Dougherty, ML Bittner, Y Chen, K Sivakumar, P Meltzer, JM Trent, General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. J. Biomed. Opt. 5(4), 411–424 (2000).\nI Shmulevich, ER Dougherty, S Kim, W Zhang, Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinforma. 18(2), 261–274 (2002).\nD Martins, U Braga-Neto, R Hashimoto, M Bittner, ER Dougherty, Intrinsically multivariate predictive genes. IEEE J. Sel. Top. Sign. Proces. 2(3), 424–439 (2008).\nT Chen, UM Braga-Neto, Statistical detection of intrinsically multivariate predictive genes. IEEE\u002FACM Trans. Comput. Biol. Bioinform. 12(4), 951–964 (2015).\nT Chen, UM Braga-Neto, Exact performance of CoD estimators in discrete prediction. EURASIP J. Adv. Signal Process (2010). (Article ID 2010:487893).\nT Chen, UM Braga-Neto, Maximum-likelihood estimation of the discrete coefficient of determination in stochastic boolean systems. IEEE Trans. Signal Process. 61(15), 3880–3894 (2013).\nT Chen, UM Braga-Neto, Statistical detection of Boolean regulatory relationships. IEEE\u002FACM Trans. Comput. Biol. Bioinform. 10(5), 1310–1321 (2013).\nLA Dalton, ER Dougherty, Bayesian minimum mean-square error estimation for classification error – Part I: Definition and the Bayesian mmse error estimator for discrete classification. IEEE Trans. Signal Process. 59(1), 115–129 (2011).\nLA Dalton, ER Dougherty, Bayesian minimum mean-square error estimation for classification error – Part II: Linear classification of gaussian models. IEEE Trans. Signal Process. 59(1), 130–144 (2011).\nT Chen, UM Braga-Neto, in In Proceedings of the 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2013). Optimal Bayesian MMSE estimation of the coefficient of determination for discrete prediction (TXHouston, Nov 2013), pp. 66–69.\nLA Dalton, ER Dougherty, Optimal classifiers with minimum expected error within a Bayesian framework – Part I: Discrete and gaussian models. Pattern Recogn. 46(5), 1301–1314 (2013).\nLA Dalton, ER Dougherty, Optimal classifiers with minimum expected error within a Bayesian framework – Part II: Properties and performance analysis. Pattern Recogn. 46(5), 1288–1300 (2013).\nL Devroye, L Gyorfi, G Lugosi, A Probabilistic Theory of Pattern Recognition (Springer, New York, 1996).\nG Casella, R Berger, Statistical Inference, 2nd ed (Pacific Grove, CA, Duxbury, 2002).\nM Bittner, P Meltzer, Y Chen, Y Jiang, E Seftor, M Hendrix, M Radmacher, R Simon, Z Yakhini, A Ben-Dor, N Sampas, ER Dougherty, F Marincola, E Wang, C Gooden, J Lueders, A Glatfelter, P Pollock, J Carpten, E Gillanders, D Leja, K Dietrich, C Beaudry, M Berens, D Alberts, V Sondak, N Hayward, J Trent, Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 406:, 536–540 (2000).\nS Kim, ER Dougherty, N Cao, Y Chen, M Bittner, E Suh, Can markov chain models mimic biological regulation?J. Biol. Syst. 10:, 437–458 (2002).\nA Datta, A Choudhary, M Bittner, ER Dougherty, External control in markovian genetic regulatory networks. Mach. Learn. 52:, 169–191 (2003).\nUM Braga-Neto, ER Dougherty, Error Estimation for Pattern Recognition (Wiley, New York, 2015).\nS Ross, A first course in probability, 4th ed (Macmillan, New York, 1994).\nG Arfken, Mathematical Methods for Physicists, 3rd ed (Academic Press, Orlando, FL, 1985).\nN Balakrishnan, V Nevzorov, A Primer on Statistical Distributions (Wiley, Hoboken, NJ, 2003).",{"VOID":75},"10.1186\u002Fs13637-015-0035-4","PUBLICATION","VERIFIED","2024-06-24T20:39:43.474+00:00","Auto Verify","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002Fs13637-015-0035-4",[82,98],{"id":83,"sortIndex":21,"researcher":20,"roles":84,"affiliations":86,"properties":95,"displayName":97,"givenName":20,"familyName":20},"d00b467b-ef2a-48df-a016-38bc416b59ce",[85],"AUTHOR",[87],{"id":88,"sortIndex":21,"affiliation":89,"properties":20},"e0c25b36-8c24-4061-a8da-a1305b30b801",{"id":88,"createTime":20,"updateTime":20,"relativeEntities":90,"slug":20,"properties":91,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":94,"statistic":20},[],{"title":92},{"VI":93},"Emmes Corporation, Rockville, USA",[],{"title":96},{"VI":97},"Ting Chen",{"id":99,"sortIndex":45,"researcher":20,"roles":100,"affiliations":101,"properties":110,"displayName":112,"givenName":20,"familyName":20},"ed127498-1f00-4493-b528-7d38a129a16b",[85],[102],{"id":103,"sortIndex":21,"affiliation":104,"properties":20},"725a07fa-4347-4fd7-85e6-ba5f6fb472ac",{"id":103,"createTime":20,"updateTime":20,"relativeEntities":105,"slug":20,"properties":106,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":109,"statistic":20},[],{"title":107},{"VI":108},"Department of Electrical and Computer Engineering, Texas A&M University, TX, USA",[],{"title":111,"gsAuthor":113},{"VI":112},"Ulisses M. Braga-Neto",{"VOID":114},"[\"TkFr66cAAAAJ\"]","ARTICLE",{"url":80,"publisher":117,"properties":137},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":118,"slug":10,"properties":119,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":123,"manageAffiliations":124,"indexDatabases":125,"url":20,"thumbnailPath":20,"statistic":132,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":120,"title":121,"eissn":122},{"VOID":13},{"EN":15},{"VOID":17},[],[],[126],{"id":26,"indexDatabase":127,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":128,"label":129,"description":130,"key":34,"publicationTags":131,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":133,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":134,"totalCitation":47,"totalCitationByYear":135,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":136,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":138,"volume":140},{"VOID":139},"1-19",{"VOID":141},"2016",2,{"total":142,"publishYear":144,"statisticByYear":145},2016,{},"2016-01-15","DONE_ANALYZE_CITATION",[39],false,{"id":151,"createTime":152,"updateTime":153,"relativeEntities":154,"slug":155,"properties":156,"entityType":76,"verifyStatus":77,"verifyTime":165,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":166,"fullTextUrl":20,"authors":167,"publicationType":115,"publisherRelationship":202,"citationCount":21,"citationInfo":227,"publishDate":229,"publishYear":144,"citationAnalyzeStatus":230,"lastCitationAnalyze":153,"indexDatabases":231,"openAccess":20,"references":232,"isForceReanalyzing":149},"7d636c2f-04e4-4717-8059-fd032e5693f0","2024-02-12T18:10:58.883+00:00","2026-07-28T03:21:07.358+00:00",[],"Incorporating-prior-knowledge-induced-from-stochastic-differential-equations-in-the-classification-of-stochastic-observations",{"abstract":157,"title":159,"gsPaper":161,"doi":163},{"EN":158},"In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.",{"EN":160},"Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations",{"VOID":162},"[\"7167075719179881035\"]",{"VOID":164},"10.1186\u002Fs13637-016-0036-y","2024-05-02T19:38:18.922+00:00","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002Fs13637-016-0036-y",[168,185],{"id":169,"sortIndex":21,"researcher":20,"roles":170,"affiliations":171,"properties":180,"displayName":182,"givenName":20,"familyName":20},"3a439a60-6d3e-4a02-a069-2ed68ab0e0b6",[85],[172],{"id":173,"sortIndex":21,"affiliation":174,"properties":20},"4e0dd739-241d-4785-9d31-8c8cd2c9097d",{"id":173,"createTime":20,"updateTime":20,"relativeEntities":175,"slug":20,"properties":176,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":179,"statistic":20},[],{"title":177},{"VI":178},"Department of Electrical and Electronic Engineering, Nazarbayev University, Astana, Kazakhstan",[],{"title":181,"gsAuthor":183},{"VI":182},"Amin Zollanvari",{"VOID":184},"[\"vUbAcUMAAAAJ\"]",{"id":186,"sortIndex":45,"researcher":20,"roles":187,"affiliations":188,"properties":197,"displayName":199,"givenName":20,"familyName":20},"50c462db-06a0-42f8-8258-a1951c2715f6",[85],[189],{"id":190,"sortIndex":21,"affiliation":191,"properties":20},"e98e461e-0d82-4816-a6d0-eb55dfd900c4",{"id":190,"createTime":20,"updateTime":20,"relativeEntities":192,"slug":20,"properties":193,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":196,"statistic":20},[],{"title":194},{"VI":195},"The Center for Bioinformatics and Genomic Systems Engineering and the Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas",[],{"title":198,"gsAuthor":200},{"VI":199},"Edward R. Dougherty",{"VOID":201},"[\"DvTVkcEAAAAJ\"]",{"url":166,"publisher":203,"properties":223},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":204,"slug":10,"properties":205,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":209,"manageAffiliations":210,"indexDatabases":211,"url":20,"thumbnailPath":20,"statistic":218,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":206,"title":207,"eissn":208},{"VOID":13},{"EN":15},{"VOID":17},[],[],[212],{"id":26,"indexDatabase":213,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":214,"label":215,"description":216,"key":34,"publicationTags":217,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":219,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":220,"totalCitation":47,"totalCitationByYear":221,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":222,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":224,"volume":226},{"VOID":225},"1-14",{"VOID":141},{"total":21,"publishYear":144,"statisticByYear":228},{},"2016-01-20","ERROR_IN_ANALYZE_CITATION",[39],[233,236,239,245,248,251,254,260,269,272,280,283,286,289,292,295,298,301,304,307,310,319,325,328,331,334,337],{"id":20,"text":234,"url":20,"identifiers":235},"Braga-Neto, UM, & Dougherty, ER. (2015). Error Estimation for Pattern Recognition. New York: Wiley-IEEE Press.",{},{"id":20,"text":237,"url":20,"identifiers":238},"Kay, S. (1993). Fundamentals of Statistical Signal Processing: Estimation Theory. New Jersey: Prentice-Hall.",{},{"id":240,"text":241,"url":242,"identifiers":243},"4c68646b-0035-4279-8000-0006b275d4fa","Carlin, BP, & Louis, TA. (2008). Bayesian Methods for Data Analysis. Boca Raton: CRC Press.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":244},"10.1007\u002Fs10440-022-00541-7",{"id":240,"text":246,"url":242,"identifiers":247},"Dalton, L, & Dougherty, ER (2011). Bayesian minimum mean-square error estimation for classification error–part I: definition and the Bayesian MMSE error estimator for discrete classification. IEEE Transactions on Signal Processing, 59(1), 115–129.",{"doi":244},{"id":240,"text":249,"url":242,"identifiers":250},"Dalton, L, & Dougherty, ER (2011). Bayesian minimum mean-square error estimation for classification error–part II: linear classification of Gaussian models. IEEE Transactions on Signal Processing, 59(1), 130–144.",{"doi":244},{"id":240,"text":252,"url":242,"identifiers":253},"Dalton, L, & Dougherty, ER (2013). Optimal classifiers with minimum expected error within a Bayesian framework – part I: discrete and Gaussian models. Pattern Recognition, 46, 1301–1314.",{"doi":244},{"id":255,"text":256,"url":257,"identifiers":258},"39723475-1f7e-4730-aa49-b096474b2df0","Dalton, L, & Dougherty, ER (2013). Optimal classifiers with minimum expected error within a Bayesian framework – part II: properties and performance analysis. Pattern Recognition, 46, 1288–1300.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320312004530",{"doi":259},"10.1016\u002Fj.patcog.2012.10.019",{"id":20,"text":261,"url":262,"identifiers":263},"Knight, J, Ivanov, I, Dougherty, ER (2014). MCMC implementation of the optimal Bayesian classifier for non-gaussian models: model-based RNA-seq classification. BMC Bioinformatics, 15. doi:10.1186\u002Fs12859-014-0401-3.","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-014-0401-3",{"mag":264,"pmc":265,"openalex":266,"pm":267,"doi":268},"2035038062","4265360","W2035038062","25491122","10.1186\u002Fs12859-014-0401-3",{"id":240,"text":270,"url":242,"identifiers":271},"Esfahani, MS, & Dougherty, ER (2014). Incorporation of biological pathway knowledge in the construction of priors for optimal Bayesian classification. IEEE\u002FACM Transactions on Computational Biology and Bioinformatics, 11, 202–218.",{"doi":244},{"id":20,"text":273,"url":274,"identifiers":275},"Esfahani, MS, & Dougherty, ER (2015). An optimization-based framework for the transformation of incomplete biological knowledge into a probabilistic structure and its application to the utilization of gene\u002Fprotein signaling pathways in discrete phenotype classification. IEEE\u002FACM Transactions on Computational Biology and Bioinformatics. doi:10.1109\u002FTCBB.2015.2424407.","https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftcbb.2015.2424407",{"mag":276,"openalex":277,"pm":278,"doi":279},"2077575889","W2077575889","26671803","10.1109\u002Ftcbb.2015.2424407",{"id":20,"text":281,"url":20,"identifiers":282},"Jaynes, ET (1968). Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, 4, 227–241.",{},{"id":240,"text":284,"url":242,"identifiers":285},"Kloeden, PE, & Platen, E. (1995). Numerical Solution of Stochastic Differential Equations. New York: Springer.",{"doi":244},{"id":20,"text":287,"url":20,"identifiers":288},"Arnold, L. (1974). Stochastic Differential Equations: Theory and Applications. New York: Wiley.",{},{"id":240,"text":290,"url":242,"identifiers":291},"Higham, D (2001). An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Review, 43, 525–546.",{"doi":244},{"id":240,"text":293,"url":242,"identifiers":294},"Anderson, TW (1951). Classification by multivariate analysis. Psychometrika, 16, 31–50.",{"doi":244},{"id":20,"text":296,"url":20,"identifiers":297},"Murphy, KP. (2012). Machine Learning: A Probabilistic Perspective. Cambridge: MIT Press.",{},{"id":20,"text":299,"url":20,"identifiers":300},"DeGroot, MH. (1970). Optimal Statistical Decisions. New York: McGrawHill.",{},{"id":240,"text":302,"url":242,"identifiers":303},"Esfahani, MS, & Dougherty, ER (2014). Effect of separate sampling on classification accuracy. Bioinformatics, 30, 242–250.",{"doi":244},{"id":240,"text":305,"url":242,"identifiers":306},"Braga-Neto, UM, Zollanvari, A, Dougherty, ER (2014). Cross-validation under separate sampling: strong bias and how to correct it. Bioinformatics, 30, 3349–3355.",{"doi":244},{"id":240,"text":308,"url":242,"identifiers":309},"Hansen, TF (1997). Stabilizing selection and the comparative analysis of adaptation. Evolution, 51, 1341–1351.",{"doi":244},{"id":20,"text":311,"url":312,"identifiers":313},"Thompson, K, & Kubatko, LS (2013). Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies. BMC Bioinformatics, 14. doi:10.1186\u002F1471-2105-14-200.","https:\u002F\u002Fdoi.org\u002F10.1186\u002F1471-2105-14-200",{"mag":314,"pmc":315,"openalex":316,"pm":317,"doi":318},"2063023900","3706278","W2063023900","23786262","10.1186\u002F1471-2105-14-200",{"id":320,"text":321,"url":322,"identifiers":323},"489d26e4-ecac-437b-a124-5a0476df2a44","Zollanvari, A, & Dougherty, ER (2014). Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model. Pattern Recognition, 47, 2178–2192.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320313005086",{"doi":324},"10.1016\u002Fj.patcog.2013.11.022",{"id":240,"text":326,"url":242,"identifiers":327},"Dalton, L, & Dougherty, ER (2014). Intrinsically optimal Bayesian robust filtering. IEEE Transactions on Signal Processing, 62(3), 657–670.",{"doi":244},{"id":240,"text":329,"url":242,"identifiers":330},"Pugachev, VS. (1965). Theory of Random Functions and Its Applications to Control Problems. Oxford: Pergamon.",{"doi":244},{"id":20,"text":332,"url":20,"identifiers":333},"Dougherty, ER. (1999). Random Processes for Image and Signal Processing. New York: SPIE Press and IEEE Presses.",{},{"id":240,"text":335,"url":242,"identifiers":336},"Higham, DJ (2015). An introduction to multilevel Monte Carlo for option valuation. International Journal of Computer Mathematics, 92(12).",{"doi":244},{"id":20,"text":338,"url":20,"identifiers":339},"Duda, RO, Hart, PE, Stork, DG. (2000). Pattern Classification. New York: Wiley.",{},{"id":341,"createTime":342,"updateTime":343,"relativeEntities":344,"slug":345,"properties":346,"entityType":76,"verifyStatus":77,"verifyTime":357,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":358,"fullTextUrl":20,"authors":359,"publicationType":115,"publisherRelationship":403,"citationCount":429,"citationInfo":430,"publishDate":433,"publishYear":431,"citationAnalyzeStatus":147,"lastCitationAnalyze":343,"indexDatabases":434,"openAccess":20,"references":20,"isForceReanalyzing":149},"ff46aa01-254b-44c4-80ec-7cae1e9d394f","2023-12-07T07:33:59.371+00:00","2026-07-10T12:03:16.530+00:00",[],"Reaction-Diffusion-Modeling-ERK-and-STAT-Interaction-Dynamics",{"abstract":347,"title":349,"gsPaper":351,"references":353,"doi":355},{"EN":348},"The modeling of the dynamics of interaction between ERK and STAT signaling pathways in the cell needs to establish the biochemical diagram of the corresponding proteins interactions as well as the corresponding reaction-diffusion scheme. Starting from the verbal description available in the literature of the cross talk between the two pathways, a simple diagram of interaction between ERK and STAT5a proteins is chosen to write corresponding kinetic equations. The dynamics of interaction is modeled in a form of two-dimensional nonlinear dynamical system for ERK—and STAT5a —protein concentrations. Then the spatial modeling of the interaction is accomplished by introducing an appropriate diffusion-reaction scheme. The obtained system of partial differential equations is analyzed and it is argued that the possibility of Turing bifurcation is presented by loss of stability of the homogeneous steady state and forms dissipative structures in the ERK and STAT interaction process. In these terms, a possible scaffolding effect in the protein interaction is related to the process of stabilization and destabilization of the dissipative structures (pattern formation) inherent to the model of ERK and STAT cross talk.",{"EN":350},"Reaction-Diffusion Modeling ERK- and STAT-Interaction Dynamics",{"VOID":352},"[\"3076698982729982497\"]",{"VOID":354},"Beltrami E: Mathematics for Dynamic Modeling. Academic Press, Boston, Mass, USA; 1987.\nEungdamrong NJ, Iyengar R: Modeling cell signaling networks. Biology of the Cell 2004,96(5):355-362. 10.1016\u002Fj.biolcel.2004.03.004\nKhurana S, Kreydiyyeh S, Aronzon A, et al.:Asymmetric signal transduction in polarized ileal -absorbing cells: carbachol activates brush-border but not basolateral-membrane -PLC and translocates PLC- only to the brush border Biochemical Journal 1996,313(2):509-518.\nHoldaway-Clarke TL, Feijo JA, Hackett GR, Kunkel JG, Hepler PK: Pollen tube growth and the intracellular cytosolic calcium gradient oscillate in phase while extracellular calcium influx is delayed. Plant Cell 1997,9(11):1999-2010.\nLam H, Matroule J-Y, Jacobs-Wagner C: The asymmetric spatial distribution of bacterial signal transduction proteins coordinates cell cycle events. Developmental Cell 2003,5(1):149-159. 10.1016\u002FS1534-5807(03)00191-6\nBelenkaya TY, Han C, Yan D, et al.: Drosophila Dpp morphogen movement is independent of dynamin-mediated endocytosis but regulated by the glypican members of heparan sulfate proteoglycans. Cell 2004,119(2):231-244. 10.1016\u002Fj.cell.2004.09.031\nLengyel I, Epstein IR: A chemical approach to designing Turing patterns in reaction-diffusion systems. Proceedings of the National Academy of Sciences of the United States of America 1992,89(9):3977-3979. 10.1073\u002Fpnas.89.9.3977\nAlber M, Glimm T, Hentschel HGE, Kazmierczak B, Newman SA:Stability of -dimensional patterns in a generalized Turing system: implications for biological pattern formation. Nonlinearity 2005,18(1):125-138. 10.1088\u002F0951-7715\u002F18\u002F1\u002F007\nPawson T, Raina M, Nash P: Interaction domains: from simple binding events to complex cellular behavior. FEBS Letters 2002,513(1):2-10. 10.1016\u002FS0014-5793(01)03292-6\nShuai K: Modulation of STAT signaling by STAT-interacting proteins. Oncogene 2000,19(21):2638-2644. 10.1038\u002Fsj.onc.1203522\nAlexander WS: Suppressors of cytokine signalling (SOCS) in the immune system. Nature Reviews Immunology 2002,2(6):410-416.\nCacalano NA, Sanden D, Johnston JA: Tyrosine-phosphorylated SOCS-3 inhibits STAT activation but binds to p120 RasGAP and activates Ras. Nature Cell Biology 2001,3(5):460-465. 10.1038\u002F35074525\nBuitenhuis M, Coffer PJ, Koenderman L: Signal transducer and activator of transcription 5 (STAT5). International Journal of Biochemistry and Cell Biology 2004,36(11):2120-2124. 10.1016\u002Fj.biocel.2003.11.008\nWood TJJ, Sliva D, Lobie PE, et al.: Mediation of growth hormone-dependent transcriptional activation by mammary gland factor\u002Fstat 5. Journal of Biological Chemistry 1995,270(16):9448-9453. 10.1074\u002Fjbc.270.16.9448\nPircher TJ, Petersen H, Gustafsson J-A, Haldosen L-A: Extracellular signal-regulated kinase (ERK) interacts with signal transducer and activator of transcription (STAT) 5a. Molecular Endocrinology 1999,13(4):555-565. 10.1210\u002Fme.13.4.555\nBlume-Jensen P, Hunter T: Oncogenic kinase signalling. Nature 2001,411(6835):355-365. 10.1038\u002F35077225\nBoulton TG, Yancopoulos GD, Gregory JS, et al.: An insulin-stimulated protein kinase similar to yeast kinases involved in cell cycle control. Science 1990,249(4964):64-67. 10.1126\u002Fscience.2164259\nBoulton TG, Nye SH, Robbins DJ, et al.: ERKs: a family of protein-serine\u002Fthreonine kinases that are activated and tyrosine phosphorylated in response to insulin and NGF. Cell 1991,65(4):663-675. 10.1016\u002F0092-8674(91)90098-J\nTakahashi K, Vel Arjunan S, Tomita M: Space in systems biology of signaling pathways - towards intracellular molecular crowding in silico. FEBS Letters 2005,579(8):1783-1788. 10.1016\u002Fj.febslet.2005.01.072\nKholodenko BN, Brown GC, Hoek JB: Diffusion control of protein phosphorylation in signal transduction pathways. Biochemical Journal 2000,350(3):901-907. 10.1042\u002F0264-6021:3500901\nBhalla US: Signaling in small subcellular volumes. I. Stochastic and diffusion effects on individual pathways. Biophysical Journal 2004,87(2):733-744. 10.1529\u002Fbiophysj.104.040469\nSchnell S, Turner TE: Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Progress in Biophysics and Molecular Biology 2004,85(2-3):235-260. 10.1016\u002Fj.pbiomolbio.2004.01.012\nSwameye I, Müller TG, Timmer J, Sandra O, Klingmüller U: Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. Proceedings of the National Academy of Sciences of the United States of America 2003,100(3):1028-1033. 10.1073\u002Fpnas.0237333100\nKetteler R, Heinrich AC, Offe JK, et al.: A functional green fluorescent protein-tagged erythropoietin receptor despite physical separation of JAK2 binding site and tyrosine residues. Journal of Biological Chemistry 2002,277(29):26547-26552. 10.1074\u002Fjbc.M202287200\nKolch W: Meaningful relationships: the regulation of the Ras\u002FRaf\u002FMEK\u002FERK pathway by protein interactions. Biochemical Journal 2000,351(2):289-305. 10.1042\u002F0264-6021:3510289\nGeorgiev N, Petrov V, Nikolova E: Systems biology of cell signalling pathways. Proceedings of the 10th Jubilee National Congress on Theoretical and Applied Mechanics, Varna, Bulgaria, September 2005 2: 120-123.\nBerg HC: Random Walks in Biology. Princeton University Press, Princeton, NJ, USA; 1993.\nNagorcka BN, Mooney JR: From stripes to spots: prepatterns which can be produced in the skin by a reaction-diffusion system. IMA Journal of Mathematics Applied in Medicine and Biology 1992,9(4):249-267. 10.1093\u002Fimammb\u002F9.4.249\nPainter KJ, Maini PK, Othmer HG: Stripe formation in juvenile Pomacanthus explained by a generalized Turing mechanism with chemotaxis. Proceedings of the National Academy of Sciences of the United States of America 1999,96(10):5549-5554. 10.1073\u002Fpnas.96.10.5549\nIooss G, Joseph DD: Elementary Stability and Bifurcation Theory. 2nd edition. Springer, New York, NY, USA; 1990.\nTichonov AN: Systemy differentsialnyh uravneniy, soderjashchie malye parametry pri proizvodnyh. Matematicheskiy Sbornik 1952,31(3):575-586.\nTuring AM: The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society B 1952, 237: 37-72. 10.1098\u002Frstb.1952.0012\nPircher TJ, Flores-Morales A, Mui AL-F, et al.: Mitogen-activated protein kinase kinase inhibition decreases growth hormone stimulated transcription mediated by STAT5. Molecular and Cellular Endocrinology 1997,133(2):169-176. 10.1016\u002FS0303-7207(97)00164-0\nStewart S, Sundaram M, Zhang Y, Lee J, Han M, Guan K-L: Kinase suppressor of Ras forms a multiprotein signaling complex and modulates MEK localization. Molecular and Cellular Biology 1999,19(8):5523-5534.\nSchaeffer HJ, Catling AD, Eblen ST, Collier LS, Krauss A, Weber MJ: MP1: a MEK binding partner that enhances enzymatic activation of the MAP kinase cascade. Science 1998,281(5383):1668-1671.\nTeis D, Wunderlich W, Huber LA: Localization of the MP1-MAPK scaffold complex to endosomes is mediated by p14 and required for signal transduction. Developmental Cell 2002,3(6):803-814. 10.1016\u002FS1534-5807(02)00364-7\nBray D, Lay S: Computer-based analysis of the binding steps in protein complex formation. Proceedings of the National Academy of Sciences of the United States of America 1997,94(25):13493-13498. 10.1073\u002Fpnas.94.25.13493\nLevchenko A, Bruck J, Sternberg PW: Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. 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networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.",{"EN":673},"Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning",{"VOID":675},"N Friedman, M Linial, I Nachman, D Pe’er, Using Bayesian networks to analyze expression data. J. Comput. Biol. 7(3–4), 601–620 (2000).\nN Friedman, Inferring cellular networks using probabilistic graphical models. Science. 303(5659), 799–805 (2004).\nAJ Hartemink, DK Gifford, TS Jaakkola, RA Young, in The Pacific Symposium on Biocomputing (PSB01). Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks (Hawaii, 2001), pp. 422–33.\nS Imoto, S Kim, T Goto, S Miyano, S Aburatani, K Tashiro, S Kuhara, Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J. Bioinforma. Comput. Biol. 1(2), 231–52 (2003).\nK Sachs, O Perez, D Pe’er, DA Lauffenburger, GP Nolan, Causal protein-signaling networks derived from multiparameter single-cell data. Science. 308(5721), 523–529 (2005).\nD Nikovski, Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Trans. Knowl. Data Eng. 12, 509–516 (2000).\nAV Nefian, L Liang, X Pi, X Liu, K Murphy, Dynamic Bayesian networks for audio-visual speech recognition. EURASIP J. Appl. Signal Process. 11(4), 1–15 (2002).\nP Weber, G Medina-Oliva, C Simon, B Iung, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25(4), 671–682 (2012).\nO Pourret, P Naïm, B Marcot (eds.), Bayesian Networks: A Practical Guide to Applications (Wiley, Chichester, UK, 2008).\nJ Pearl, Causality: Models, Reasoning and Inference, 2nd edn. (Cambridge University Press, New York, NY, USA, 2009).\nM Grzegorczyk, D Husmeier, Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach. Learn. 71(2–3), 265–305 (2008).\nN Friedman, D Koller, Being Bayesian about network structure – a Bayesian approach to structure discovery in Bayesian networks. Mach. Learn. 50(1–2), 95–125 (2003).\nB Ellis, WH Wong, Learning causal Bayesian network structures from experimental data. J. Am. Stat. Assoc. 103(482), 778–789 (2008).\nT Niinimäki, M Koivisto, in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Annealed importance sampling for structure learning in Bayesian networks (AAAI PressBeijing, China, 2013), pp. 1579–1585.\nM Koivisto, K Sood, Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res. 5, 549–573 (2004).\nD Eaton, K Murphy, in UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence. Bayesian structure learning using dynamic programming and MCMC (Morgan KaufmannVancouver, 2007), pp. 101–108.\nJ Pearl, in Proceedings of the 7th Conference of the Cognitive Science Society. Bayesian networks: a model of self-activated memory for evidential reasoning (University of CaliforniaIrvine, 1985), pp. 329–334.\nGF Cooper, E Herskovits, A Bayesian method for the induction of probabilistic networks from data. Mach. learn. 9(4), 309–347 (1992).\nD Heckerman, D Geiger, DM Chickering, Learning Bayesian networks: the combination of knowledge and statistical data. Mach. learn. 20(3), 197–243 (1995).\nD Geiger, D Heckerman, A characterization of the bivariate Wishart distribution. Probab. Math. Stat. 18(1), 119–131 (1998).\nD Madigan, J York, Bayesian graphical models for discrete data. Int. Stat. Rev. 63(2), 215–232 (1995).\nMK Cowles, BP Carlin, Markov chain Monte Carlo convergence diagnostics: a comparative review. J. Am. Stat. Assoc. 91(434), 883–904 (1996).\nA Gelman, in Practical Markov Chain Monte Carlo, ed. by W Gilks, S Richardson, and D Spiegelhalter. Inference and monitoring convergence (Chapman and HallLondon, 1996), pp. 131–143.\nR Castelo, T Koc̆ka, On inclusion-driven learning of Bayesian networks. J. Mach. Learn. Res. 4, 527–574 (2003).\nAW Moore, W-K Wong, in Proceedings of the Twentieth International Conference on Machine Learning. Optimal reinsertion: a new search operator for accelerated and more accurate Bayesian network structure learning (Washington D.C., US, 2003), pp. 552–559.\nDM Chickering, Learning equivalence classes of Bayesian-network structures. J. Mach. Learn. Res. 2, 445–498 (2002).\nD Madigan, SA Andersson, MD Perlman, CT Volinsky, Bayesian model averaging and model selection for Markov equivalence classes of acyclic digraphs. Commun. Stat. Theory and Methods. 25, 2493–2519 (1996).\nP Giudici, R Castelo, Improving Markov chain Monte Carlo model search for data mining. Mach. Learn. 50(1–2), 127–158 (2003).\nH Haario, M Laine, A Mira, E Saksman, DRAM: Efficient adaptive MCMCDRAM: Efficient adaptive MCMC. Stat. Comput. 16, 339–354 (2006).\nIA Beinlich, HJ Suermondt, RM Chavez, GF Cooper, in Second European Conference on Artificial Intelligence in Medicine, 38. The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks (SpringerLondon, Great Britain, 1989), pp. 247–256.",{"VOID":677},"10.1186\u002Fs13637-015-0024-7","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002Fs13637-015-0024-7",[680,704],{"id":681,"sortIndex":21,"researcher":20,"roles":682,"affiliations":683,"properties":701,"displayName":703,"givenName":20,"familyName":20},"5c9f30ab-5958-49d4-b5e8-14835d29d5cb",[85],[684,692],{"id":685,"sortIndex":21,"affiliation":686,"properties":20},"f8a53b44-3a21-414d-834b-af46d676c4d7",{"id":685,"createTime":20,"updateTime":20,"relativeEntities":687,"slug":20,"properties":688,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":691,"statistic":20},[],{"title":689},{"VI":690},"Department of Information and Computer Science, Aalto University, FI-00076Aalto, Finland",[],{"id":693,"sortIndex":45,"affiliation":694,"properties":700},"0a5c9b4f-efa7-4a48-a3e5-19c96d3568a1",{"id":693,"createTime":20,"updateTime":20,"relativeEntities":695,"slug":20,"properties":696,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":699,"statistic":20},[],{"title":697},{"VI":698},"Department of Signal Processing, Tampere University of Technology, Tampere, Finland",[],{},{"title":702},{"VI":703},"Antti Larjo",{"id":705,"sortIndex":45,"researcher":20,"roles":706,"affiliations":707,"properties":723,"displayName":725,"givenName":20,"familyName":20},"912e39ff-42b8-402e-9645-dbc8f9e8f4c7",[85],[708,714],{"id":685,"sortIndex":21,"affiliation":709,"properties":20},{"id":685,"createTime":20,"updateTime":20,"relativeEntities":710,"slug":20,"properties":711,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":713,"statistic":20},[],{"title":712},{"VI":690},[],{"id":715,"sortIndex":45,"affiliation":716,"properties":722},"450633a6-ab55-478c-a2ab-289541a45119",{"id":715,"createTime":20,"updateTime":20,"relativeEntities":717,"slug":20,"properties":718,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":721,"statistic":20},[],{"title":719},{"VI":720},"Turku Centre for Biotechnology, Turku University, Turku, Finland",[],{},{"title":724},{"VI":725},"Harri Lähdesmäki",{"url":678,"publisher":727,"properties":747},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":728,"slug":10,"properties":729,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":733,"manageAffiliations":734,"indexDatabases":735,"url":20,"thumbnailPath":20,"statistic":742,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":730,"title":731,"eissn":732},{"VOID":13},{"EN":15},{"VOID":17},[],[],[736],{"id":26,"indexDatabase":737,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":738,"label":739,"description":740,"key":34,"publicationTags":741,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":743,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":744,"totalCitation":47,"totalCitationByYear":745,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":746,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":748,"volume":749},{"VOID":225},{"VOID":750},"2015","2015-06-20",2015,[39],{"id":755,"createTime":756,"updateTime":757,"relativeEntities":758,"slug":759,"properties":760,"entityType":76,"verifyStatus":77,"verifyTime":757,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":769,"fullTextUrl":20,"authors":770,"publicationType":115,"publisherRelationship":799,"citationCount":20,"citationInfo":20,"publishDate":825,"publishYear":826,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":827,"openAccess":20,"references":20,"isForceReanalyzing":149},"b9393897-a269-414d-b369-29e4ccb4e1a5","2023-12-05T15:11:56.394+00:00","2025-02-23T21:44:24.886+00:00",[],"A-novel-cost-function-to-estimate-parameters-of-oscillatory-biochemical-systems",{"abstract":761,"title":763,"references":765,"doi":767},{"EN":762},"Oscillatory pathways are among the most important classes of biochemical systems with examples ranging from circadian rhythms and cell cycle maintenance. Mathematical modeling of these highly interconnected biochemical networks is needed to meet numerous objectives such as investigating, predicting and controlling the dynamics of these systems. Identifying the kinetic rate parameters is essential for fully modeling these and other biological processes. These kinetic parameters, however, are not usually available from measurements and most of them have to be estimated by parameter fitting techniques. One of the issues with estimating kinetic parameters in oscillatory systems is the irregularities in the least square (LS) cost function surface used to estimate these parameters, which is caused by the periodicity of the measurements. These irregularities result in numerous local minima, which limit the performance of even some of the most robust global optimization algorithms. We proposed a parameter estimation framework to address these issues that integrates temporal information with periodic information embedded in the measurements used to estimate these parameters. This periodic information is used to build a proposed cost function with better surface properties leading to fewer local minima and better performance of global optimization algorithms. We verified for three oscillatory biochemical systems that our proposed cost function results in an increased ability to estimate accurate kinetic parameters as compared to the traditional LS cost function. We combine this cost function with an improved noise removal approach that leverages periodic characteristics embedded in the measurements to effectively reduce noise. The results provide strong evidence on the efficacy of this noise removal approach over the previous commonly used wavelet hard-thresholding noise removal methods. This proposed optimization framework results in more accurate kinetic parameters that will eventually lead to biochemical models that are more precise, predictable, and controllable.",{"EN":764},"A novel cost function to estimate parameters of oscillatory biochemical systems",{"VOID":766},"Goldbeter A: Biochemical Oscillations and Cellular Rhythms the Molecular Bases of Periodic and Chaotic Behaviour. Cambridge University Press, Cambridge; 1996.\nFall C, Marland E, Tyson J: Computational Cell Biology. Springer, New York; 2002.\nPerez-Martin J: Growth and development eukaryotes. Current Opinion Microbiol 2010, 13(6):661-662. 10.1016\u002Fj.mib.2010.10.007\nYan J, Wang H, Liu Y, Shao C: Analysis of gene regulatory networks in the mammalian circadian rhythm. PLos Comput Biol 2008, 4(10):e1000193. 10.1371\u002Fjournal.pcbi.1000193\nCollins K, Jacks T, Pavletich N: The cell cycle and cancer. PNAS: Proc Natl Acad Sci 1997, 94(7):2776-2778. 10.1073\u002Fpnas.94.7.2776\nBoullin J, Morgan JM: The development of cardiac rhythm. Heart 2005, 91(7):874-875. 10.1136\u002Fhrt.2004.047415\nPerry J: The Ovarian Cycle of Mammals. Oliver and Boyd, Edinburgh; 1971.\nZaccolo M, Pozzan T: cAMP and Ca2+ interplay: a matter of oscillation patterns. Trends Neurosci 2003, 26(2):53-55. 10.1016\u002FS0166-2236(02)00017-6\nBagheri N, Lawson M, Stelling J, Doyle F: Modeling the Drosophila melanogaster circadian oscillator via Phase optimization. J Biol Rhythms 2008, 23(6):525-537. 10.1177\u002F0748730408325041\nZeilinger M, Farre E, Taylor S, Kay S, Doyle F: A novel computational model of the circadian clock in Arabidopsis that incorporates PRR7 and PRR9. Mol Syst Biol 2006., 2(58):\nLocke J, Millar A, Turner M: Modelling genetic networks with noisy and varied experimental data the circadian clock in Arabidopsis thaliana. J Theor Biol 2005, 234(3):383-393. 10.1016\u002Fj.jtbi.2004.11.038\nRodriguez-Fernandez M, Mendes P, Banga J: A hybrid approach for efficient and robust parameter estimation in biochemical pathways. BioSystems 2005, 83: 248-265.\nVyshemirsky V, Girolami M: Bayesian ranking of biochemical system models. Bioinformatics 2008, 24(6):833-839. 10.1093\u002Fbioinformatics\u002Fbtm607\nChou IC, Voit E: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 2009, 219(2):57-83. 10.1016\u002Fj.mbs.2009.03.002\nMostacci E, Truntzer C, Cardot H, Ducoroy P: Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data. Proteomics 2010, 10(14):2564-2572. 10.1002\u002Fpmic.200900185\nTang G, Qin A: ECG de-noising based on empirical mode decomposition. The 9th International Conference for Young Computer Scientists, 2008. ICYCS 2008, 903-906.\nRen Z, Liu G, Zeng L, Huang Z, Huang S: Research on biochemical spectrum denoising based on a novel wavelet threshold function and an improved translation-invariance method. Proc SPIE 2008, 7280: 72801Q.\nSugimoto M, Kikuchi S, Tomita M: Reverse engineering of biochemical equations from time-course data by means of genetic programming. Biosystems 2005, 80(2):155-164. 10.1016\u002Fj.biosystems.2004.11.003\nGonzalez O, Kuper C, Jung K, Naval JP, Mendoza E: Parameter estimation using simulated annealing for S-system models of biochemical networks. Bioinformatics 2007, 23(4):480-486. 10.1093\u002Fbioinformatics\u002Fbtl522\nFlaherty P, Radhakrishnan M, Dinh T, Rebres R, Roach T, Jordan M, Arkin A: A dual receptor crosstalk model of g-protein-coupled signal transduction. PLoS Comput Biol 2008, 4(9):e1000185. 10.1371\u002Fjournal.pcbi.1000185\nZhan C, Yeung L: Parameter estimation in systems biology models using spline approximation. BMC Syst Biol 2011., 5(14):\nMarquardt D: An algorithm for least squares estimation of nonlinear parameters. SIAM J Appl Math 1963, 11(2):431-441. 10.1137\u002F0111030\nRenders J, Flasse S: Hybrid methods using genetic algorithms for global optimization. IEEE Trans Syst Man Cybernet Part B, Cybernet 1996, 26(2):243-258. 10.1109\u002F3477.485836\nGerhard D: Pitch extraction and fundamental frequency history and current techniques. Department of Computer Science, University of Regina, Regina, Canada 2003.\nTyson J, Hong C, Thron D, Novak B: A simple model of circadian rhythm based on dimerization and proteolysis of PER and TIM. Biophys J 1999, 77: 2411-2417. 10.1016\u002FS0006-3495(99)77078-5\nKondepudi D, Prigogine I: Modern Thermodynamics from Heat Engines to Dissipative Structures. Wiley, Chichester; 1998.\nGoldbeter A: A model for circadian oscillations in the drosophila period protein (PER). Proc Royal Soc B, Biol Sci 1995, 261(1362):319-324. 10.1098\u002Frspb.1995.0153\nMallat S: A Wavelet Tour of Signal Processing. American Press, San Diego; 1998.\nMallat S: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal Mach Intell 1989, 11(7):674-693. 10.1109\u002F34.192463\nCheveigne A, Kawahara H: Yin, a fundamental frequency estimator for speech and music. J Acoust Soc Am 2002, 111(4):1917-1930. 10.1121\u002F1.1458024\nMoles C, Mendes P, Banga J: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 2003, 13(11):2467-2474. 10.1101\u002Fgr.1262503\nInc TM: MATLAB: version 7.6.0. Natick Massachusetts 2008.\nLagarias J, Reeds J, Wright M, Wright P: Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J Optim 1998, 9: 112-147. 10.1137\u002FS1052623496303470\nLe Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B, Snoep JL, Hucka M: BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 2006, 34(suppl 1):D689-D691.\nGutenkunst R, Waterfall J, Casey F, Brown K, Myers C, Sethna J: Universally Sloppy Parameter Sensitivities in Systems Biology Models. PLos Comput Biol 2005, 3(10):1871-1878.\nWaterfall J, Casey F, Gutenkunst R, Brown K, Myers C, Brouwer P, Elser V, Sethna J: Sloppy-model universality class and the Vandermonde matrix. Phys Rev Lett 2006, 97(15):150601.\nApgar J, Witmer D, Whitead F, Tidor B: Sloppy models, parameter uncertainty, and the role of experimental design. Mol BioSyst 2010, 6(10):1890-1900. 10.1039\u002Fb918098b",{"VOID":768},"10.1186\u002F1687-4153-2012-3","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002F1687-4153-2012-3",[771,786],{"id":772,"sortIndex":21,"researcher":20,"roles":773,"affiliations":774,"properties":783,"displayName":785,"givenName":20,"familyName":20},"44e591c4-107a-4d81-b183-7b819d0f0e89",[85],[775],{"id":776,"sortIndex":21,"affiliation":777,"properties":20},"1e7ce658-c7a4-4c26-8691-088d7787c7ff",{"id":776,"createTime":20,"updateTime":20,"relativeEntities":778,"slug":20,"properties":779,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":782,"statistic":20},[],{"title":780},{"VI":781},"Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA",[],{"title":784},{"VI":785},"Seyedbehzad Nabavi",{"id":787,"sortIndex":45,"researcher":20,"roles":788,"affiliations":789,"properties":796,"displayName":798,"givenName":20,"familyName":20},"00ad1809-ec96-4057-9d06-04a7aa13392e",[85],[790],{"id":776,"sortIndex":21,"affiliation":791,"properties":20},{"id":776,"createTime":20,"updateTime":20,"relativeEntities":792,"slug":20,"properties":793,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":795,"statistic":20},[],{"title":794},{"VI":781},[],{"title":797},{"VI":798},"Cranos M Williams",{"url":769,"publisher":800,"properties":820},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":801,"slug":10,"properties":802,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":806,"manageAffiliations":807,"indexDatabases":808,"url":20,"thumbnailPath":20,"statistic":815,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":803,"title":804,"eissn":805},{"VOID":13},{"EN":15},{"VOID":17},[],[],[809],{"id":26,"indexDatabase":810,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":811,"label":812,"description":813,"key":34,"publicationTags":814,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":816,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":817,"totalCitation":47,"totalCitationByYear":818,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":819,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":821,"volume":823},{"VOID":822},"1-17",{"VOID":824},"2012","2012-05-16",2012,[39],{"id":829,"createTime":830,"updateTime":831,"relativeEntities":832,"slug":833,"properties":834,"entityType":76,"verifyStatus":77,"verifyTime":843,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":844,"fullTextUrl":20,"authors":845,"publicationType":115,"publisherRelationship":861,"citationCount":20,"citationInfo":20,"publishDate":887,"publishYear":888,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":889,"openAccess":20,"references":20,"isForceReanalyzing":149},"580d9858-7be6-4493-b28f-498d057eb99e","2023-12-06T14:25:24.587+00:00","2025-02-22T11:43:45.156+00:00",[],"A-Hypothesis-Test-for-Equality-of-Bayesian-Network-Models",{"abstract":835,"title":837,"references":839,"doi":841},{"EN":836},"Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between varying phenotypes. In principle, separately fit models can be directly compared, but it is difficult to assign statistical significance to any observed differences. There would therefore be an advantage to the development of a rigorous hypothesis test for homogeneity of network structure. In this paper, a generalized likelihood ratio test based on Bayesian network models is developed, with significance level estimated using permutation replications. In order to be computationally feasible, a number of algorithms are introduced. First, a method for approximating multivariate distributions due to Chow and Liu (1968) is adapted, permitting the polynomial-time calculation of a maximum likelihood Bayesian network with maximum indegree of one. Second, sequential testing principles are applied to the permutation test, allowing significant reduction of computation time while preserving reported error rates used in multiple testing. The method is applied to gene-set analysis, using two sets of experimental data, and some advantage to a pathway modelling approach to this problem is reported.",{"EN":838},"A Hypothesis Test for Equality of Bayesian Network Models",{"VOID":840},"Dougherty ER, Shmulevich I, Chen J, Wang ZJ: Genomic Signal Processing and Statistics, EURASIP Book Series on Signal Processing and Communications. Volume 2. Hindawi Publishing Corporation, New York, NY, USA; 2005.\nShmulevich I, Dougherty ER: Genomic Signal Processing. Princeton University Press, Princeton, NJ, USA; 2007.\nEmmert-Streib F, Dehmer M: Detecting pathological pathways of a complex disease by a comparitive analysis of networks. In Analysis of Microarray Data: A Network-Based Approach. Edited by: Emmert-Streib F, Dehmer M. Wiley-VCH, Weinheim, Germany; 2008:285-305.\nMootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1 α -responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics 2003, 34(3):267-273. 10.1038\u002Fng1180\nSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(43):15545-15550. 10.1073\u002Fpnas.0506580102\nSubramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP: GSEA-P: a desktop application for gene set enrichment analysis. Bioinformatics 2007, 23(23):3251-3253. 10.1093\u002Fbioinformatics\u002Fbtm369\nSebastiani P, Abad M, Ramoni MF: Bayesian networks for genomic analysis. In Genomic Signal Processing and Statistics, EURASIP Book Series on Signal Processing and Communications. Edited by: Dougherty ER, Shmulevich I, Chen J, Wang ZJ. Hindawi Publishing Corporation, New York, NY, USA; 2005.\nFriedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7(3-4):601-620. 10.1089\u002F106652700750050961\nNeedham CJ, Bradford JR, Bulpitt AJ, Westhead DR: A primer on learning in Bayesian networks for computational biology. PLoS Computational Biology 2007, 3(8):e129. 10.1371\u002Fjournal.pcbi.0030129\nChu T, Glymour C, Scheines R, Spirtes P: A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. 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Proceedings of the National Academy of Sciences of the United States of America 2002, 99(20):12783-12788. 10.1073\u002Fpnas.192159399\nBraun R, Cope L, Parmigiani G: Identifying differential correlation in gene\u002Fpathway combinations. BMC Bioinformatics 2008., 9: article no. 488\nBarry WT, Nobel AB, Wright FA: Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005, 21(9):1943-1949. 10.1093\u002Fbioinformatics\u002Fbti260\nJiang Z, Gentleman R: Extensions to gene set enrichment. Bioinformatics 2007, 23(3):306-313. 10.1093\u002Fbioinformatics\u002Fbtl599\nKlebanov L, Glazko G, Salzman P, Yakovlev A, Xiao Y: A multivariate extension of the gene set enrichment analysis. Journal of Bioinformatics and Computational Biology 2007, 5(5):1139-1153. 10.1142\u002FS0219720007003041\nGoeman JJ, Bühlmann P: Analyzing gene expression data in terms of gene sets: methodological issues. 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BMC Bioinformatics 2007., 8: article 242\nAlmudevar A: A simulated annealing algorithm for maximum likelihood pedigree reconstruction. Theoretical Population Biology 2003, 63(2):63-75. 10.1016\u002FS0040-5809(02)00048-5",{"VOID":842},"10.1155\u002F2010\u002F947564","2025-02-22T11:43:45.155+00:00","http:\u002F\u002Fbsb.eurasipjournals.com\u002Fcontent\u002F2010\u002F1\u002F947564",[846],{"id":847,"sortIndex":21,"researcher":20,"roles":848,"affiliations":849,"properties":858,"displayName":860,"givenName":20,"familyName":20},"b8336a1a-a64e-4e60-9b37-31db7c2bd19b",[85],[850],{"id":851,"sortIndex":21,"affiliation":852,"properties":20},"49fea16a-dff1-4dec-9eb4-8ce779ed5ded",{"id":851,"createTime":20,"updateTime":20,"relativeEntities":853,"slug":20,"properties":854,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":857,"statistic":20},[],{"title":855},{"VI":856},"Department of Computational Biology, University of Rochester, Rochester, USA",[],{"title":859},{"VI":860},"Anthony Almudevar",{"url":844,"publisher":862,"properties":882},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":863,"slug":10,"properties":864,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":868,"manageAffiliations":869,"indexDatabases":870,"url":20,"thumbnailPath":20,"statistic":877,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":865,"title":866,"eissn":867},{"VOID":13},{"EN":15},{"VOID":17},[],[],[871],{"id":26,"indexDatabase":872,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":873,"label":874,"description":875,"key":34,"publicationTags":876,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":878,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":879,"totalCitation":47,"totalCitationByYear":880,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":881,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":883,"volume":885},{"VOID":884},"1-11",{"VOID":886},"2010","2010-08-09",2010,[39],{"id":891,"createTime":892,"updateTime":893,"relativeEntities":894,"slug":895,"properties":896,"entityType":76,"verifyStatus":77,"verifyTime":893,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":905,"fullTextUrl":20,"authors":906,"publicationType":115,"publisherRelationship":963,"citationCount":20,"citationInfo":20,"publishDate":989,"publishYear":990,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":991,"openAccess":20,"references":20,"isForceReanalyzing":149},"6e6e3c61-35ce-431d-b1f0-e0d9aa7048ce","2024-01-11T05:29:44.658+00:00","2025-02-21T08:25:56.185+00:00",[],"Learning-directed-acyclic-graphs-from-large-scale-genomics-data",{"abstract":897,"title":899,"references":901,"doi":903},{"EN":898},"In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.",{"EN":900},"Learning directed acyclic graphs from large-scale genomics data",{"VOID":902},"A Shojaie, G Michailidis, Discovering graphical Granger causality using the truncating lasso penalty. 26 ECCB 2010:, i517–i523 (2010). Department of Statistics, University of Michigan, ECCB, Vol.26.\nA Battle, MC Jonikas, P Walter, JS Weissman, D Koller, Automated identification of pathways from quantitative genetic interaction data. Mol.Syst. Biol. 6:, 379–391 (2010).\nAHY Tong, et al, Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science. 294:, 2364–2368 (2001).\nB Snijder, P Liberali, M Frechin, T Stoeger, L Pelkmans, Predicting functional gene interactions with the hierarchical interaction score. Nat. Methods. 10(11), 1089–1094 (2013).\nA Baryshinkova, et al, Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods. 7:, 1017–1024 (2010).\nSR Collins, A Roguev, NJ Krogan, Quantitative genetic interaction mapping using the E-MAP approach. Methods Enzymol. 470:, 205–231 (2010).\nRO Linden, VP Eronen, T Aittokallio, Quantitative maps of genetic interactions in yeast—comparative evaluation and integrative analysis. BMC Syst. Biol. 5:, 45–58 (2011).\nSJ Dixon, M Constanzo, A Baryshinkova, B Andrews, C Boone, Systematic mapping of genetic interaction networks. Annu.Rev. Genet. 43:, 601–625 (2009).\nGN Brock, et al, Methods for detecting gene gene interaction in multiplex extended pedigrees. BMC Genet. 6:, 144–149 (2005).\nTC Hu, AB Kahng, Linear and integer programming in practice (Springer International Publishing, Schweiz, 2016). ISBN-10: 3319239996.\nG Sierksma, Linear and integer programming: theory and practice, second edition (CRC Press, Boca Raton, 2001). ISBN-10: 0824706730.\nG Sierksma, Y Zwols, Linear and integer optimization: theory and practice, third edition (CRC Press, Boca Raton, 2015). ISBN-10: 1498710166.\nE Demirel, N Demirel, H Gökcen, A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. J. Clean. Prod. 112:, 1813–2144 (2016).\nCH Antunes, MJ Alves, J Climaco, Multiobjective linear and integer programming (Springer International Publishing, Schweiz, 2016). ISBN-13: 9783319287447.\nM Diaby, MH Karwan, Advances in combinatorial optimization (World Scientific Publishing Co. Pte. Ltd., Singapore, 2016). ISBN-10: 9814704873.\nR Diestel, Graphentheorie (Springer-Verlag, Heidelberg, 2012). ISBN 978-3-642-14911-5.\nA Jaimovich, et al, Modularity and directionality in genetic interaction maps. Nat. Methods. 26:, 38–45 (2010).\nA Baryshinkova, M Constanzo, CL Myers, B Andrews, C Boone, Genetic interaction networks: toward an understanding of heritability. Annu.Rev. Genomics Hum. Genet. 14:, 111–133 (2013).\nA Rogueav, et al, Quantitative genetic-interaction mapping in mammalian cells. Nat. Methods. 10:, 432–437 (2013).\nM Constanzo, et al, The genetic landscape of a cell. Science. 327:, 425–431 (2010).\nF Nikolay, M Pesavento, Learning directed-acyclic-graphs from large-scale double-knockout experiments (C, Communications System Group, TU Darmstadt, EUSIPCO, 2016). Budapest, August – September 2016.\nV Balakrishnan, S Boyd, S Balemi, Branch and bound algorithm for computing the minimum stability degree of parameter-dependent linear systems. Int. J. Robust Nonlinear Control. 1(4), 295–317 (1991).\nEL Lawler, DE Wood, Branch-and-bound methods: a survey. Oper. Res. 14:, 699–719 (1966).\nRE Moore, Global optimization to prescribed accuracy. Comput. Math. 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ISBN 0486402584.\nSupplementary Material. https:\u002F\u002Fwww2.spg.tu-darmstadt.de\u002Ffnikolay\u002Fsupp_journal.pdf.\nCVX – A Matlab based convex modeling framework. http:\u002F\u002Fcvxr.com.\nMOSEK Solver. https:\u002F\u002Fwww.mosek.com\u002F.\nM Babu, et al, Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli. PLoS Genet. 10:, 400–414 (2014).\nSGD - Saccharomyces genome database. http:\u002F\u002Fwww.yeastgenome.org.\nM Costanzo, et al, DRYGIN - Data repository of yeast genetic interactions. Terence Donnelly Centre for Cellular and Biochemical Research, University of Toronto. http:\u002F\u002Fdrygin.ccbr.utoronto.ca\u002F~costanzo2009\u002Fx.",{"VOID":904},"10.1186\u002Fs13637-017-0063-3","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002Fs13637-017-0063-3",[907,922,935,950],{"id":908,"sortIndex":21,"researcher":20,"roles":909,"affiliations":910,"properties":919,"displayName":921,"givenName":20,"familyName":20},"13651ed1-7d04-4c7d-b581-89a0e7ac0f02",[85],[911],{"id":912,"sortIndex":21,"affiliation":913,"properties":20},"9479e2e8-0c6c-4349-864e-21b9a9aea7aa",{"id":912,"createTime":20,"updateTime":20,"relativeEntities":914,"slug":20,"properties":915,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":918,"statistic":20},[],{"title":916},{"VI":917},"Communication Systems Group, TU Darmstadt, Darmstadt, Germany",[],{"title":920},{"VI":921},"Fabio Nikolay",{"id":923,"sortIndex":45,"researcher":20,"roles":924,"affiliations":925,"properties":932,"displayName":934,"givenName":20,"familyName":20},"d6d9a9d3-6696-48dc-a746-56ae740f8116",[85],[926],{"id":912,"sortIndex":21,"affiliation":927,"properties":20},{"id":912,"createTime":20,"updateTime":20,"relativeEntities":928,"slug":20,"properties":929,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":931,"statistic":20},[],{"title":930},{"VI":917},[],{"title":933},{"VI":934},"Marius Pesavento",{"id":936,"sortIndex":142,"researcher":20,"roles":937,"affiliations":938,"properties":947,"displayName":949,"givenName":20,"familyName":20},"5eb88ad9-e73c-4164-955f-3435cba58880",[85],[939],{"id":940,"sortIndex":21,"affiliation":941,"properties":20},"ea64defb-59cc-4ce8-9784-e4ecf88a314c",{"id":940,"createTime":20,"updateTime":20,"relativeEntities":942,"slug":20,"properties":943,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":946,"statistic":20},[],{"title":944},{"VI":945},"European Molecular Biology Laboratory, Heidelberg, Heidelberg, Germany",[],{"title":948},{"VI":949},"George Kritikos",{"id":951,"sortIndex":429,"researcher":20,"roles":952,"affiliations":953,"properties":960,"displayName":962,"givenName":20,"familyName":20},"cc6fc08e-2d20-429e-b78b-ea0cf63c3aea",[85],[954],{"id":940,"sortIndex":21,"affiliation":955,"properties":20},{"id":940,"createTime":20,"updateTime":20,"relativeEntities":956,"slug":20,"properties":957,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":959,"statistic":20},[],{"title":958},{"VI":945},[],{"title":961},{"VI":962},"Nassos Typas",{"url":905,"publisher":964,"properties":984},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":965,"slug":10,"properties":966,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":970,"manageAffiliations":971,"indexDatabases":972,"url":20,"thumbnailPath":20,"statistic":979,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":967,"title":968,"eissn":969},{"VOID":13},{"EN":15},{"VOID":17},[],[],[973],{"id":26,"indexDatabase":974,"url":37,"indexYears":38,"academicFieldIds":20,"indexDatabaseRanking":39},{"id":28,"createTime":20,"updateTime":20,"relativeEntities":975,"label":976,"description":977,"key":34,"publicationTags":978,"standard":20},[],{"EN":31,"VI":31},{"EN":31,"VI":33},[36],{"impactFactor":21,"impactFactorByYear":980,"i10Index":42,"i10IndexLast5Year":21,"totalPublication":43,"totalPublicationByYear":981,"totalCitation":47,"totalCitationByYear":982,"totalCitationPerPublication":52,"totalCitationPerPublicationByYear":983,"hindexLast5Year":42,"hindex":42},{},{"2006":45,"2008":45,"2009":46,"2010":45},{"2006":49,"2008":50,"2009":51,"2010":45},{"2006":49,"2008":50,"2009":54,"2010":45},{"pages":985,"volume":987},{"VOID":986},"1-16",{"VOID":988},"2017","2017-09-20",2017,[39],{"id":993,"createTime":994,"updateTime":995,"relativeEntities":996,"slug":997,"properties":998,"entityType":76,"verifyStatus":77,"verifyTime":1007,"verifyNote":79,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1008,"fullTextUrl":20,"authors":1009,"publicationType":115,"publisherRelationship":1095,"citationCount":20,"citationInfo":20,"publishDate":1120,"publishYear":826,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":1121,"openAccess":20,"references":20,"isForceReanalyzing":149},"cd84c37a-934c-44fa-b1b5-472851998885","2024-01-13T03:15:03.295+00:00","2025-02-21T00:23:49.724+00:00",[],"A-visual-analytics-approach-for-models-of-heterogeneous-cell-populations",{"abstract":999,"title":1001,"references":1003,"doi":1005},{"EN":1000},"In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.",{"EN":1002},"A visual analytics approach for models of heterogeneous cell populations",{"VOID":1004},"Avery S: Microbial cell individuality and the underlying sources of heterogeneity. Nat Rev Microbiol 2006, 4: 577-587. 10.1038\u002Fnrmicro1460\nSnijder B, Pelkmans L: Origins of regulated cell-to-cell variability. Nat Rev Mol Cell Biol 2011, 12(2):119-25. 10.1038\u002Fnrm3044\nEldar A, Elowitz M: Functional roles for noise in genetic circuits. Nature 2010, 467(9):1-7. 10.1038\u002Fnj7319-1\nAlbeck J, Burke J, Spencer S, Lauffenburger D, Sorger P: Modeling a snap-action, variable-delay switch controlling extrinsic cell death. PLoS Biol 2008, 6(12):2831-2852.\nSpencer S, Gaudet S, Albeck J, Burke J, Sorger P: Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 2009, 459(7245):428-433. 10.1038\u002Fnature08012\nNiepel M, Spencer S, Sorger P: Non-genetic cell-to-cell variability and the consequences for pharmacology. Cur Opin Biotechnol 2009, 13(5-6):556-561.\nSingh D, Ku CJ, Wichaidit C, Steininger R, Wu L, Altschuler S: Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol Syst Biol 2010, 6(369):1-10.\nPaulsson J: Models of stochastic gene expression. Phys Life Rev 2005, 2(2):157-175. 10.1016\u002Fj.plrev.2005.03.003\nGlauche I, Moore K, Thielecke L, Horn K, Loeffler M, Roeder I: Stem cell proliferation and quiescence — two sides of the same coin. PLoS Comput Biol 2009, 5(7):e1000447. [http:\u002F\u002Fwww.ploscompbiol.org\u002Farticle\u002Finfo:doi\u002F10.1371\u002Fjournal.pcbi.1000447] 10.1371\u002Fjournal.pcbi.1000447\nHuh D, Paulsson J: Non-genetic heterogeneity from stochastic partitioning at cell division. 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Bioinf 2006, 22(4):514-515. 10.1093\u002Fbioinformatics\u002Fbti799",{"VOID":1006},"10.1186\u002F1687-4153-2012-4","2025-02-21T00:23:49.723+00:00","https:\u002F\u002Fbsb-eurasipjournals.springeropen.com\u002Farticles\u002F10.1186\u002F1687-4153-2012-4",[1010,1025,1040,1055,1068,1082],{"id":1011,"sortIndex":21,"researcher":20,"roles":1012,"affiliations":1013,"properties":1022,"displayName":1024,"givenName":20,"familyName":20},"0dd07faf-5145-4419-b1f9-ef1fecc8e98b",[85],[1014],{"id":1015,"sortIndex":21,"affiliation":1016,"properties":20},"584f56d2-b555-4d3e-800f-e9b19c05c613",{"id":1015,"createTime":20,"updateTime":20,"relativeEntities":1017,"slug":20,"properties":1018,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1021,"statistic":20},[],{"title":1019},{"VI":1020},"Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany",[],{"title":1023},{"VI":1024},"Jan 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of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, they are used for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI's combined DNA index system (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats—an application of importance in genetic profiling.",{"EN":1132},"Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates",{"VOID":1134},"citation_journal_title=Bioinformatics; citation_title=The mutual information: detecting and evaluating dependencies between variables; citation_author=R Steuer, J Kurths, CO Daub, J Weise, J Selbig; citation_volume=18; citation_issue=supplement 2; citation_publication_date=2002; citation_pages=S231-S240; citation_doi=10.1093\u002Fbioinformatics\u002F18.suppl_2.S231; citation_id=CR1\ncitation_journal_title=IEEE\u002FACM Transactions on Computational Biology and Bioinformatics; citation_title=Gene mapping and marker clustering using Shannon's mutual information; citation_author=Z Dawy, B Goebel, J Hagenauer, C Andreoli, T Meitinger, JC Mueller; citation_volume=3; citation_issue=1; citation_publication_date=2006; citation_pages=47-56; citation_doi=10.1109\u002FTCBB.2006.9; citation_id=CR2\ncitation_journal_title=Nature; citation_title=A genomic code for nucleosome positioning; citation_author=E Segal, Y Fondufe-Mittendorf, L Chen; citation_volume=442; citation_issue=7104; citation_publication_date=2006; citation_pages=772-778; citation_doi=10.1038\u002Fnature04979; citation_id=CR3\ncitation_journal_title=Gene; citation_title=Comparative analysis of base correlations in \n                      \n                     untranslated regions of various species; citation_author=Y Osada, R Saito, M Tomita; citation_volume=375; citation_issue=1-2; citation_publication_date=2006; citation_pages=80-86; citation_doi=10.1016\u002Fj.gene.2006.02.018; citation_id=CR4\ncitation_journal_title=Gene; citation_title=Initiation of translation in prokaryotes and eukaryotes; citation_author=M Kozak; citation_volume=234; citation_issue=2; citation_publication_date=1999; citation_pages=187-208; citation_doi=10.1016\u002FS0378-1119(99)00210-3; citation_id=CR5\ncitation_journal_title=Genomics, Proteomics and Bioinformatics; citation_title=Comparative analysis of transcription start sites using mutual information; citation_author=DA Reddy, CK Mitra; citation_volume=4; citation_issue=3; citation_publication_date=2006; citation_pages=189-195; citation_doi=10.1016\u002FS1672-0229(06)60032-6; citation_id=CR6\ncitation_journal_title=Computational Biology and Chemistry; citation_title=Comparative analysis of core promoter region: information content from mono and dinucleotide substitution matrices; citation_author=DA Reddy, BVLS Prasad, CK Mitra; citation_volume=30; citation_issue=1; citation_publication_date=2006; citation_pages=58-62; citation_doi=10.1016\u002Fj.compbiolchem.2005.10.004; citation_id=CR7\ncitation_journal_title=Nucleic Acids Research; citation_title=Comparative analysis of orthologous eukaryotic mRNAs: potential hidden functional signals; citation_author=SA Shabalina, AY Ogurtsov, IB Rogozin, EV Koonin, DJ Lipman; citation_volume=32; citation_issue=5; citation_publication_date=2004; citation_pages=1774-1782; citation_doi=10.1093\u002Fnar\u002Fgkh313; citation_id=CR8\ncitation_journal_title=Bioinformatics; citation_title=Exploiting the past and the future in protein secondary structure prediction; citation_author=P Baldi, S Brunak, P Frasconi, G Soda, G Pollastri; citation_volume=15; citation_issue=11; citation_publication_date=1999; citation_pages=937-946; citation_doi=10.1093\u002Fbioinformatics\u002F15.11.937; citation_id=CR9\ncitation_journal_title=IEEE Engineering in Medicine and Biology Magazine; citation_title=Should genetics get an information-theoretic education? 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