{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:39:42Z","timestamp":1785803982396,"version":"3.56.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Since many important biological systems or processes are dynamic systems, it is important to study the gene expression patterns over time in a genomic scale in order to capture the dynamic behavior of gene expression. Microarray technologies have made it possible to measure the gene expression levels of essentially all the genes during a given biological process. In order to determine the transcriptional factors (TFs) involved in gene regulation during a given biological process, we propose to develop a functional response model with varying coefficients in order to model the transcriptional effects on gene expression levels and to develop a group smoothly clipped absolute deviation (SCAD) regression procedure for selecting the TFs with varying coefficients that are involved in gene regulation during a biological process.<\/jats:p><jats:p>Results: Simulation studies indicated that such a procedure is quite effective in selecting the relevant variables with time-varying coefficients and in estimating the coefficients. Application to the yeast cell cycle microarray time course gene expression data set identified 19 of the 21 known TFs related to the cell cycle process. In addition, we have identified another 52 TFs that also have periodic transcriptional effects on gene expression during the cell cycle process. Compared to simple linear regression (SLR) analysis at each time point, our procedure identified more known cell cycle related TFs.<\/jats:p><jats:p>Conclusions: The proposed group SCAD regression procedure is very effective for identifying variables with time-varying coefficients, in particular, for identifying the TFs that are related to gene expression over time. By identifying the TFs that are related to gene expression variations over time, the procedure can potentially provide more insight into the gene regulatory networks.<\/jats:p><jats:p>Contact: \u00a0hli@cceb.upenn.edu<\/jats:p><jats:p>Supplementary information: \u00a0http:\/\/www.cceb.med.upenn.edu\/~hli\/gSCAD-Appendix.pdf<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm125","type":"journal-article","created":{"date-parts":[[2007,4,27]],"date-time":"2007-04-27T00:29:34Z","timestamp":1177633774000},"page":"1486-1494","source":"Crossref","is-referenced-by-count":210,"title":["Group SCAD regression analysis for microarray time course gene expression data"],"prefix":"10.1093","volume":"23","author":[{"given":"Lifeng","family":"Wang","sequence":"first","affiliation":[{"name":"1 Department of Biostatistics and Epidemiology, 2Department of Bioengineering and 3Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics and Epidemiology, 2Department of Bioengineering and 3Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzhe","family":"Li","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics and Epidemiology, 2Department of Bioengineering and 3Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA"},{"name":"1 Department of Biostatistics and Epidemiology, 2Department of Bioengineering and 3Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2007,4,26]]},"reference":[{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1093\/nar\/gkg894","article-title":"Identifying cooperativity among transcription factors controlling the cell cycle in yeast","volume":"31","author":"Banerjee","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser B"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/84792","article-title":"Regulatory element detection using correlation with expression","volume":"27","author":"Bussemaker","year":"2001","journal-title":"Nat. Genet"},{"issue":"1","key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"R4","DOI":"10.1186\/gb-2007-8-1-r4","article-title":"Clustering of genes into regulons using integrated moeling(cogrim)","volume":"8","author":"Chen","year":"2007","journal-title":"Genome Biol"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"3339","DOI":"10.1073\/pnas.0630591100","article-title":"Integrating regulatory motif discovery and genome-wide expression analysis","volume":"100","author":"Conlon","year":"2003","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","DOI":"10.1038\/msb4100067","article-title":"Adaptively inferring human transcriptional subnetworks","author":"Das","year":"2006","journal-title":"Mol. Syst. Biol"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1093\/bioinformatics\/18.9.1167","article-title":"Identification of regulatory elements using a feature selection method","volume":"18","author":"Keles","year":"2002","journal-title":"Bioinformatics"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Stat"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1198\/016214501753382273","article-title":"Variable slection via nonconcave penalized likelihood and its oracle properties","volume":"96","author":"Fan","year":"2001","journal-title":"J. Am. Stat. Assoc"},{"key":"2023041105083274900_","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"2001","journal-title":"Ann. Stat"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/1471-2105-5-31","article-title":"Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data","volume":"5","author":"Gao","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1111\/j.1541-0420.2005.00505.x","article-title":"Functional hierarchical models for identifying genes with different time-course expression profiles","volume":"62","author":"Hong","year":"2006","journal-title":"Biometrics"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1126\/science.1075090","article-title":"Transcriptional regulatory networks in S. cerevisiae","volume":"298","author":"Lee","journal-title":"Science"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1093\/bioinformatics\/btg014","article-title":"Clustering of time-course gene expression data using a mixed-effects model with B-splines","volume":"19","author":"Luan","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1093\/nar\/gkl013","article-title":"A data-driven clustering method for time course gene expression data","volume":"34","author":"Ma","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1091\/mbc.9.12.3273","article-title":"Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization","volume":"9","author":"Spellman","year":"1998","journal-title":"Mol. Biol. Cell"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"12837","DOI":"10.1073\/pnas.0504609102","article-title":"Significance analysis of time course microarray experiments","volume":"102","author":"Storey","year":"2005","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","DOI":"10.1214\/009053606000000759","article-title":"A multivariate empirical Bayes statistic for replicated microarray time course data","author":"Tai","year":"2006","journal-title":"Ann. Stat"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. B"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"13532","DOI":"10.1073\/pnas.0505874102","article-title":"Statistical methods for identifying yeast cell cycle transcription factors","volume":"102","author":"Tsai","year":"2005","journal-title":"PNAS"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","DOI":"10.1198\/016214505000000394","article-title":"Hidden Markov models for microarray time course data in multiple biological conditions","author":"Yuan","year":"2006","journal-title":"J. Am. Stat. Assoc"},{"key":"2023041105083274900_","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","article-title":"Model selection and estimation in regression with grouped variables","volume":"68","author":"Yuan","year":"2006","journal-title":"J. R. Stat. Soc. B"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/12\/1486\/49814406\/bioinformatics_23_12_1486.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/12\/1486\/49814406\/bioinformatics_23_12_1486.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T22:50:53Z","timestamp":1736981453000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/23\/12\/1486\/223749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,4,26]]},"references-count":22,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2007,6,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btm125","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2007,6,15]]},"published":{"date-parts":[[2007,4,26]]}}}