{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:57:19Z","timestamp":1777528639634,"version":"3.51.4"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers.<\/jats:p>\n               <jats:p>Results: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data.<\/jats:p>\n               <jats:p>Availability: Source code written in R is available from<\/jats:p>\n               <jats:p>Contact: \u00a0hongyu.zhao@yale.edu<\/jats:p>\n               <jats:p>Supplementary Information: Supplementary Data are available at<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl344","type":"journal-article","created":{"date-parts":[[2006,6,30]],"date-time":"2006-06-30T14:27:07Z","timestamp":1151677627000},"page":"2028-2036","source":"Crossref","is-referenced-by-count":189,"title":["Pathway analysis using random forests classification and regression"],"prefix":"10.1093","volume":"22","author":[{"given":"Herbert","family":"Pang","sequence":"first","affiliation":[{"name":"Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine 1 \u00a0 1 \u00a0 \u00a0 New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiping","family":"Lin","sequence":"additional","affiliation":[{"name":"W. M. Keck Biotechnology Resource Laboratory, Yale University School of Medicine 2 \u00a0 2 \u00a0 \u00a0 New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Holford","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine 1 \u00a0 1 \u00a0 \u00a0 New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradley E.","family":"Enerson","sequence":"additional","affiliation":[{"name":"Boyer Center for Molecular Medicine, Yale University School of Medicine 3 \u00a0 3 \u00a0 \u00a0 New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Lu","sequence":"additional","affiliation":[{"name":"Pfizer Groton Laboratories, Safety Sciences 5 \u00a0 5 \u00a0 \u00a0 Groton, CT 06340, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael P.","family":"Lawton","sequence":"additional","affiliation":[{"name":"Pfizer Groton Laboratories, Safety Sciences 5 \u00a0 5 \u00a0 \u00a0 Groton, CT 06340, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugenia","family":"Floyd","sequence":"additional","affiliation":[{"name":"Pfizer Groton Laboratories, Safety Sciences 5 \u00a0 5 \u00a0 \u00a0 Groton, CT 06340, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine 1 \u00a0 1 \u00a0 \u00a0 New Haven, CT 06520, USA"},{"name":"W. M. Keck Biotechnology Resource Laboratory, Yale University School of Medicine 2 \u00a0 2 \u00a0 \u00a0 New Haven, CT 06520, USA"},{"name":"Department of Genetics, Yale University School of Medicine 4 \u00a0 4 \u00a0 \u00a0 New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,6,29]]},"reference":[{"key":"2023012409101316800_b1","doi-asserted-by":"crossref","first-page":"2764","DOI":"10.1046\/j.1432-1327.2001.02225.x","article-title":"Post-translational modifications and activation of p53 by genotoxic stresses","volume":"268","author":"Appella","year":"2001","journal-title":"Eur. J. Biochem."},{"key":"2023012409101316800_b2","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nm733","article-title":"Gene-expression profiles predict survival of patients with lung adenocarcinoma","volume":"8","author":"Beer","year":"2002","journal-title":"Nat. Med."},{"key":"2023012409101316800_b3","doi-asserted-by":"crossref","first-page":"13790","DOI":"10.1073\/pnas.191502998","article-title":"Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses","volume":"98","author":"Bhattacharjee","year":"2001","journal-title":"Proc. Natl Aacd. Sci. USA"},{"key":"2023012409101316800_b4","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1161\/01.ATV.0000186181.19909.a6","article-title":"12\/15-lipoxygenase regulates intercellular adhesion molecule-1 expression and monocyte adhesion to endothelium through activation of RhoA and nuclear factor-\u03baB","volume":"25","author":"Bolick","year":"2005","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"2023012409101316800_b5","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/bioinformatics\/btg419","article-title":"Is cross-validation valid for small-sample microarray classification?","volume":"20","author":"Braga-Neto","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b6","volume-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"2023012409101316800_b7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"2023012409101316800_b8","article-title":"Manual on setting up, using, and understanding Random Forests V4.0","author":"Breiman","year":"2003"},{"key":"2023012409101316800_b9","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/nature03479","article-title":"X-inactivation profile reveals extensive variability in X-linked gene expression in females","volume":"434","author":"Carrel","year":"2005","journal-title":"Nature"},{"key":"2023012409101316800_b10","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1161\/01.RES.0000146672.10582.17","article-title":"Chemokines in the pathogenesis of vascular disease","volume":"95","author":"Charo","year":"2004","journal-title":"Circ. Res."},{"key":"2023012409101316800_b11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.tibtech.2005.05.011","article-title":"Pathways to the analysis of microarray data","volume":"23","author":"Curtis","year":"2005","journal-title":"Trends Biotechnol."},{"key":"2023012409101316800_b12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1023\/A:1025036530605","article-title":"Nitric oxide modulates MCP-1 expression in endothelial cells: implications for the pathogenesis of pulmonary granulomatous vasculitis","volume":"27","author":"Desail","year":"2003","journal-title":"Inflammation"},{"key":"2023012409101316800_b13","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1093\/bioinformatics\/bth447","article-title":"BagBoosting for tumor classification with gene expression data","volume":"20","author":"Dettling","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1159\/000071572","article-title":"Escape from X inactivation","volume":"99","author":"Disteche","year":"2002","journal-title":"Cytogenet. Genome Res."},{"key":"2023012409101316800_b15","first-page":"548","article-title":"Improvements on cross-validation: The .632+ Bootstrap estimator","volume":"92","author":"Efron","year":"1997","journal-title":"J. Am. Stat. Assoc."},{"key":"2023012409101316800_b16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/01926230500512068","article-title":"Acute drug-induced vascular injury in beagle dogs: pathology and correlating genomic expression","volume":"34","author":"Enerson","year":"2006","journal-title":"Toxicol. Pathol."},{"key":"2023012409101316800_b17","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/0962935031000097754","article-title":"Adenosine deaminase enzyme activity is increased and negatively correlates with catalase, superoxide dismutase and glutathione peroxidase in patients with Behcet's disease: original contributions\/clinical and laboratory investigations","volume":"12","author":"Erkilic","year":"2003","journal-title":"Mediators Inflamm."},{"key":"2023012409101316800_b18","doi-asserted-by":"crossref","first-page":"4660","DOI":"10.1038\/sj.onc.1208561","article-title":"Identification of molecular apocrine breast tumours by microarray analysis","volume":"24","author":"Farmer","year":"2005","journal-title":"Oncogene"},{"key":"2023012409101316800_b19","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1093\/bioinformatics\/bti294","article-title":"Estimating misclassification error with small samples via bootstrap cross-validation","volume":"21","author":"Fu","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b20","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1093\/bioinformatics\/16.10.906","article-title":"Support vector machine classification and validation of cancer tissue samples using microarray expression data","volume":"16","author":"Furey","year":"2000","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b21","doi-asserted-by":"crossref","first-page":"D258","DOI":"10.1093\/nar\/gkh036","article-title":"The Gene Ontology (GO) database and informatics resource","volume":"32","author":"Harris","year":"2004","journal-title":"Nucleic Acids Res."},{"issue":"10","key":"2023012409101316800_b22","doi-asserted-by":"crossref","first-page":"R70","DOI":"10.1186\/gb-2003-4-10-r70","article-title":"Identifying biological themes within lists of genes with EASE","volume":"4","author":"Hosack","year":"2003","journal-title":"Genome Biol."},{"key":"2023012409101316800_b23","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1046\/j.1356-9597.2001.00512.x","article-title":"HIF-1-induced apoptosis of endothelial cells","volume":"7","author":"Iida","year":"2002","journal-title":"Genes Cells"},{"key":"2023012409101316800_b24","doi-asserted-by":"crossref","first-page":"D277","DOI":"10.1093\/nar\/gkh063","article-title":"The KEGG resource for deciphering the genome","volume":"32","author":"Kanehisa","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023012409101316800_b25","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"2023012409101316800_b26","doi-asserted-by":"crossref","first-page":"D54","DOI":"10.1093\/nar\/gki031","article-title":"Entrez Gene: gene-centered information at NCBI","volume":"33","author":"Maglott","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"2023012409101316800_b27","doi-asserted-by":"crossref","first-page":"11259","DOI":"10.1158\/0008-5472.CAN-05-2495","article-title":"Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis","volume":"65","author":"Mehra","year":"2005","journal-title":"Cancer Res."},{"key":"2023012409101316800_b28","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1093\/bioinformatics\/bti499","article-title":"Prediction error estimation: a comparison of resampling methods","volume":"21","author":"Molinaro","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b29","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/ng1180","article-title":"PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes","volume":"34","author":"Mootha","year":"2003","journal-title":"Nat. Genet."},{"issue":"1","key":"2023012409101316800_b30","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/bcr552","article-title":"BAD: a good therapeutic target?","volume":"5","author":"Motoyama","year":"2003","journal-title":"Breast Cancer Res."},{"key":"2023012409101316800_b31","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1681\/ASN.2005040364","article-title":"Compartment-Specific Expression and Function of the Chemokine IP-10\/CXCL10 in a model of renal endothelial microvascular injury","volume":"17","author":"Panzer","year":"2006","journal-title":"J. Am. Soc. Nephrol."},{"key":"2023012409101316800_b32","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s00280-004-0943-6","article-title":"Therapeutic effect of tamoxifen and energy-modulating vitamins on carbohydrate-metabolizing enzymes in breast cancer","volume":"56","author":"Perumal","year":"2005","journal-title":"Cancer Chemother. Pharmacol."},{"key":"2023012409101316800_b33","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1002\/prot.20865","article-title":"Evaluation of different biological data and computational classification methods for use in protein interaction prediction","volume":"63","author":"Qi","year":"2006","journal-title":"Proteins"},{"key":"2023012409101316800_b34","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1093\/bioinformatics\/bti069","article-title":"Inferring pathways from gene lists using a literature-derived network of biological relationships","volume":"21","author":"Rajagopalan","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b35","first-page":"26:194","article-title":"Differential expression of chemokines, risk of stable coronary heart disease, and correlation with established cardiovascular risk markers","volume":"26","author":"Rothenbacher","year":"2005","journal-title":"Arterioscler. Thromb. Vascular Biol."},{"key":"2023012409101316800_b36","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1093\/oxfordjournals.jbchem.a022051","article-title":"Changes of gene expression by lysophosphatidylcholine in vascular endothelial cells: 12 up-regulated distinct genes including 5 cell growth-related, 3 thrombosis-related, and 4 others","volume":"123","author":"Sato","year":"1998","journal-title":"J. Biochem"},{"key":"2023012409101316800_b37","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: a software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res."},{"key":"2023012409101316800_b38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/bcr742","article-title":"Advances in estrogen receptor biology: Prospects for improvements in targeted breast cancer therapy","volume":"6","author":"Shao","year":"2004","journal-title":"Breast Cancer Res."},{"key":"2023012409101316800_b39","doi-asserted-by":"crossref","first-page":"10393","DOI":"10.1073\/pnas.1732912100","article-title":"Breast cancer classification and prognosis based on gene expression profiles from a population-based study","volume":"100","author":"Sotiriou","year":"2003","journal-title":"Proc. Natl Aacd. Sci. USA"},{"key":"2023012409101316800_b40","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012409101316800_b41","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"2023012409101316800_b42","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1093\/bioinformatics\/btg210","article-title":"Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data","volume":"19","author":"Wu","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012409101316800_b43","doi-asserted-by":"crossref","first-page":"9991","DOI":"10.1073\/pnas.1732008100","article-title":"A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma","volume":"100","author":"Wright","year":"2003","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012409101316800_b44","doi-asserted-by":"crossref","first-page":"30844","DOI":"10.1074\/jbc.M404651200","article-title":"Casein kinase 1alpha interacts with retinoid X receptor and interferes with agonist-induced apoptosis","volume":"279","author":"Zhao","year":"2004","journal-title":"J. Biol. Chem."}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/16\/2028\/48838427\/bioinformatics_22_16_2028.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/16\/2028\/48838427\/bioinformatics_22_16_2028.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T09:43:31Z","timestamp":1674553411000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/22\/16\/2028\/208861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,6,29]]},"references-count":44,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2006,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btl344","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2006,8,15]]},"published":{"date-parts":[[2006,6,29]]}}}