{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T15:57:13Z","timestamp":1723132633658},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":480,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.<\/jats:p>\n               <jats:p>Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.<\/jats:p>\n               <jats:p>Availability and implementation: All code is available for download at: http:\/\/homepage.tudelft.nl\/53a60\/resources\/FERAL\/FERAL.zip.<\/jats:p>\n               <jats:p>Contact: \u00a0j.deridder@tudelft.nl<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv255","type":"journal-article","created":{"date-parts":[[2015,6,13]],"date-time":"2015-06-13T17:12:36Z","timestamp":1434215556000},"page":"i311-i319","source":"Crossref","is-referenced-by-count":36,"title":["FERAL: network-based classifier with application to breast cancer outcome prediction"],"prefix":"10.1093","volume":"31","author":[{"given":"Amin","family":"Allahyar","sequence":"first","affiliation":[{"name":"Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeroen","family":"de Ridder","sequence":"additional","affiliation":[{"name":"Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,6,10]]},"reference":[{"key":"2023020115420982900_btv255-B1","doi-asserted-by":"crossref","first-page":"4947","DOI":"10.1242\/jcs.02714","article-title":"Scale-free networks in cell biology","volume":"118","author":"Albert","year":"2005","journal-title":"J. Cell Sci."},{"key":"2023020115420982900_btv255-B2","first-page":"247","article-title":"Integrating protein family sequence similarities with gene expression to find signature gene networks in breast cancer metastasis","volume-title":"6th IAPR International Conference, Pattern Recognition in Bioinformatics (PRIB)","author":"Babaei","year":"2011"},{"key":"2023020115420982900_btv255-B3","first-page":"241","article-title":"Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data","volume":"12","author":"Chen","year":"2002","journal-title":"Stat. Sin."},{"key":"2023020115420982900_btv255-B4","doi-asserted-by":"crossref","first-page":"i139","DOI":"10.1093\/bioinformatics\/btu293","article-title":"Graph-regularized dual lasso for robust eqtl mapping","volume":"30","author":"Cheng","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020115420982900_btv255-B5","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/msb4100180","article-title":"Network-based classification of breast cancer metastasis","volume":"3","author":"Chuang","year":"2007","journal-title":"Mol. Syst. Biol."},{"key":"2023020115420982900_btv255-B6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/1471-2105-13-69","article-title":"Prognostic gene signatures for patient stratification in breast cancer\u2014accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions","volume":"13","author":"Cun","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2023020115420982900_btv255-B7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/nmeth.1284","article-title":"Literature-curated protein interaction datasets","volume":"6","author":"Cusick","year":"2008","journal-title":"Nat. Methods"},{"key":"2023020115420982900_btv255-B8","doi-asserted-by":"crossref","first-page":"i625","DOI":"10.1093\/bioinformatics\/btq393","article-title":"Inferring cancer subnetwork markers using density-constrained biclustering","volume":"26","author":"Dao","year":"2010","journal-title":"Bioinformatics"},{"key":"2023020115420982900_btv255-B9","doi-asserted-by":"crossref","first-page":"4603","DOI":"10.18632\/oncotarget.2209","article-title":"Deregulation of the egfr\/pi3k\/pten\/akt\/mtorc1 pathway in breast cancer: possibilities for therapeutic intervention","volume":"5","author":"Davis","year":"2014","journal-title":"Oncotarget"},{"key":"2023020115420982900_btv255-B10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/bioinformatics\/bth469","article-title":"Outcome signature genes in breast cancer: is there a unique set?","volume":"21","author":"Ein-Dor","year":"2005","journal-title":"Bioinformatics"},{"key":"2023020115420982900_btv255-B12","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1186\/bcr1530","article-title":"Mouse models of breast cancer metastasis","volume":"8","author":"Fantozzi","year":"2006","journal-title":"Breast Cancer Res."},{"key":"2023020115420982900_btv255-B13","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1128\/MCB.21.3.794-810.2001","article-title":"Multifaceted regulation of cell cycle progression by estrogen: regulation of cdk inhibitors and cdc25a independent of cyclin d1-cdk4 function","volume":"21","author":"Foster","year":"2001","journal-title":"Mol. Cell. Biol."},{"key":"2023020115420982900_btv255-B14","author":"Friedman","year":"2010"},{"key":"2023020115420982900_btv255-B15","first-page":"2187","article-title":"Trace lasso: a trace norm regularization for correlated designs","volume-title":"Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011","author":"Grave","year":"2011"},{"key":"2023020115420982900_btv255-B16","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-35488-8","volume-title":"Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)","author":"Guyon","year":"2006"},{"key":"2023020115420982900_btv255-B17","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0092-8674(00)81683-9","article-title":"The hallmarks of cancer","volume":"100","author":"Hanahan","year":"2000","journal-title":"Cell"},{"key":"2023020115420982900_btv255-B18","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"2023020115420982900_btv255-B19","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.patcog.2008.08.001","article-title":"Performance of feature-selection methods in the classification of high-dimension data","volume":"42","author":"Hua","year":"2009","journal-title":"Pattern Recognit."},{"key":"2023020115420982900_btv255-B20","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1073\/pnas.1214014110","article-title":"High throughput kinase inhibitor screens reveal trb3 and mapk-erk\/tgf pathways as fundamental notch regulators in breast cancer","volume":"110","author":"Izrailit","year":"2013","journal-title":"Proc. Natl. Acad. Sci. U S A"},{"key":"2023020115420982900_btv255-B21","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1038\/nbt1096","article-title":"Systematic interpretation of genetic interactions using protein networks","volume":"23","author":"Kelley","year":"2005","journal-title":"Nat. Biotechnol."},{"key":"2023020115420982900_btv255-B22","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1093\/bib\/bbs037","article-title":"Batch effect removal methods for microarray gene expression data integration: a survey","volume":"14","author":"Lazar","year":"2013","journal-title":"Brief. Bioinform."},{"key":"2023020115420982900_btv255-B23","doi-asserted-by":"crossref","first-page":"e1000217","DOI":"10.1371\/journal.pcbi.1000217","article-title":"Inferring pathway activity toward precise disease classification","volume":"4","author":"Lee","year":"2008","journal-title":"PLoS Comput. Biol."},{"key":"2023020115420982900_btv255-B24","volume-title":"SLEP: Sparse Learning with Efficient Projections","author":"Liu","year":"2009"},{"key":"2023020115420982900_btv255-B25","doi-asserted-by":"crossref","first-page":"3448","DOI":"10.1093\/bioinformatics\/bti551","article-title":"Bingo: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks","volume":"21","author":"Maere","year":"2005","journal-title":"Bioinformatics"},{"key":"2023020115420982900_btv255-B26","doi-asserted-by":"crossref","first-page":"e115103","DOI":"10.1371\/journal.pone.0115103","article-title":"The value of tumor infiltrating lymphocytes (tils) for predicting response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis","volume":"9","author":"Mao","year":"2014","journal-title":"PLoS One"},{"key":"2023020115420982900_btv255-B27","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1093\/biostatistics\/kxl002","article-title":"Averaged gene expressions for regression","volume":"8","author":"Park","year":"2007","journal-title":"Biostatistics"},{"key":"2023020115420982900_btv255-B28","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00280-012-2002-z","article-title":"Prognostic significance of racgap1 mRNA expression in high-risk early breast cancer: a study in primary tumors of breast cancer patients participating in a randomized hellenic cooperative oncology group trial","volume":"71","author":"Pliarchopoulou","year":"2013","journal-title":"Cancer Chemother. Pharmacol."},{"key":"2023020115420982900_btv255-B29","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1038\/ng.2007.2","article-title":"Network modeling links breast cancer susceptibility and centrosome dysfunction","volume":"39","author":"Pujana","year":"2007","journal-title":"Nat. Genet."},{"key":"2023020115420982900_btv255-B30","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1056\/NEJM200106283442607","article-title":"Side effects of adjuvant treatment of breast cancer","volume":"344","author":"Shapiro","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"2023020115420982900_btv255-B31","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/1471-2164-5-94","article-title":"Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data","volume":"5","author":"Shen","year":"2004","journal-title":"BMC Genomics"},{"key":"2023020115420982900_btv255-B32","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/10618600.2012.681250","article-title":"A sparse-group lasso","volume":"22","author":"Simon","year":"2013","journal-title":"J. Comput. Graphical Stat."},{"key":"2023020115420982900_btv255-B33","doi-asserted-by":"crossref","first-page":"e100335","DOI":"10.1371\/journal.pone.0100335","article-title":"Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation","volume":"9","author":"Soneson","year":"2014","journal-title":"PLoS One"},{"key":"2023020115420982900_btv255-B34","doi-asserted-by":"crossref","first-page":"e34796","DOI":"10.1371\/journal.pone.0034796","article-title":"A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer","volume":"7","author":"Staiger","year":"2012","journal-title":"PloS One"},{"key":"2023020115420982900_btv255-B35","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3389\/fgene.2013.00289","article-title":"Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis","volume":"4","author":"Staiger","year":"2013","journal-title":"Front. Genet."},{"key":"2023020115420982900_btv255-B36","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/0046-8177(95)90039-X","article-title":"Breast cancer heterogeneity: evaluation of clonality in primary and metastatic lesions","volume":"26","author":"Symmans","year":"1995","journal-title":"Hum. Pathol."},{"key":"2023020115420982900_btv255-B37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/nbt.1522","article-title":"Dynamic modularity in protein interaction networks predicts breast cancer outcome","volume":"27","author":"Taylor","year":"2009","journal-title":"Nat. Biotechnol."},{"key":"2023020115420982900_btv255-B38","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1056\/NEJMoa021967","article-title":"A gene-expression signature as a predictor of survival in breast cancer","volume":"347","author":"Van De Vijver","year":"2002","journal-title":"N. Engl. J. Med."},{"key":"2023020115420982900_btv255-B39","first-page":"188","article-title":"Integrating protein-protein interaction networks with gene-gene co-expression networks improves gene signatures for classifying breast cancer metastasis","volume":"8","author":"Van den Akker","year":"2011","journal-title":"J. Integr. Bioinform."},{"key":"2023020115420982900_btv255-B40","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1186\/1471-2164-9-375","article-title":"Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability","volume":"9","author":"van Vliet","year":"2008","journal-title":"BMC Genomics"},{"key":"2023020115420982900_btv255-B41","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"van\u2019t Veer","year":"2002","journal-title":"Nature"},{"key":"2023020115420982900_btv255-B42","doi-asserted-by":"crossref","first-page":"e1002240","DOI":"10.1371\/journal.pcbi.1002240","article-title":"Most random gene expression signatures are significantly associated with breast cancer outcome","volume":"7","author":"Venet","year":"2011","journal-title":"PLoS Comput. Biol."},{"key":"2023020115420982900_btv255-B43","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1038\/nature750","article-title":"Comparative assessment of large-scale data sets of protein\u2013protein interactions","volume":"417","author":"Von Mering","year":"2002","journal-title":"Nature"},{"key":"2023020115420982900_btv255-B44","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1038\/nrc1670","article-title":"Breast cancer metastasis: markers and models","volume":"5","author":"Weigelt","year":"2005","journal-title":"Nat. Rev. Cancer"},{"key":"2023020115420982900_btv255-B45","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 (Stat. Methodol.)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/12\/i311\/49013593\/bioinformatics_31_12_i311.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/12\/i311\/49013593\/bioinformatics_31_12_i311.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:07:33Z","timestamp":1675296453000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/31\/12\/i311\/216252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,10]]},"references-count":44,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2015,6,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv255","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2015,6,15]]},"published":{"date-parts":[[2015,6,10]]}}}