{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:10:56Z","timestamp":1772172656115,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008429","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000}}],"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are best for optimizing classification performance and which algorithms are best suited to this task. Further, performance assessments based on held-out test data are lacking. We systematically compare five popular classification algorithms using gene ontology and gene expression datasets as features to predict the pro-longevity versus anti-longevity status of genes for two model organisms (\n                    <jats:italic>C. elegans<\/jats:italic>\n                    and\n                    <jats:italic>S. cerevisiae<\/jats:italic>\n                    ) using the GenAge database as ground truth. We find that elastic net penalized logistic regression performs particularly well at this task. Using elastic net, we make novel predictions of pro- and anti-longevity genes that are not currently in the GenAge database.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008429","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T14:19:16Z","timestamp":1606745956000},"page":"e1008429","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Identifying longevity associated genes by integrating gene expression and curated annotations"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0320-6787","authenticated-orcid":true,"given":"F. William","family":"Townes","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3440-6941","authenticated-orcid":true,"given":"Kareem","family":"Carr","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7718-1581","authenticated-orcid":true,"given":"Jeffrey W.","family":"Miller","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"issue":"9","key":"pcbi.1008429.ref001","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1111\/j.1532-5415.1992.tb01993.x","article-title":"The Search for the Genetic Basis of Aging: The Identification of Gerontogenes in the Nematode Caenorhabditis Elegans","volume":"40","author":"TE Johnson","year":"1992","journal-title":"Journal of the American Geriatrics Society"},{"issue":"11","key":"pcbi.1008429.ref002","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1111\/j.1558-5646.2012.01710.x","article-title":"Genomic Basis of Aging and Life-History Evolution in Drosophila Melanogaster","volume":"66","author":"SC Remolina","year":"2012","journal-title":"Evolution"},{"issue":"6","key":"pcbi.1008429.ref003","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.cell.2013.05.039","article-title":"The Hallmarks of Aging","volume":"153","author":"C L\u00f3pez-Ot\u00edn","year":"2013","journal-title":"Cell"},{"issue":"8","key":"pcbi.1008429.ref004","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1038\/s41573-020-0067-7","article-title":"The Quest to Slow Ageing through Drug Discovery","volume":"19","author":"L Partridge","year":"2020","journal-title":"Nature Reviews Drug Discovery"},{"issue":"13","key":"pcbi.1008429.ref005","doi-asserted-by":"crossref","first-page":"7394","DOI":"10.1073\/pnas.96.13.7394","article-title":"Neurosecretory Control of Aging in Caenorhabditis Elegans","volume":"96","author":"M Ailion","year":"1999","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"D1","key":"pcbi.1008429.ref006","doi-asserted-by":"crossref","first-page":"D1083","DOI":"10.1093\/nar\/gkx1042","article-title":"Human Ageing Genomic Resources: New and Updated Databases","volume":"46","author":"R Tacutu","year":"2018","journal-title":"Nucleic Acids Research"},{"issue":"D1","key":"pcbi.1008429.ref007","doi-asserted-by":"crossref","first-page":"D330","DOI":"10.1093\/nar\/gky1055","article-title":"The Gene Ontology Resource: 20 Years and Still GOing Strong","volume":"47","author":"Gene Ontology Consortium","year":"2019","journal-title":"Nucleic Acids Research"},{"issue":"2","key":"pcbi.1008429.ref008","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10522-017-9683-y","article-title":"A Review of Supervised Machine Learning Applied to Ageing Research","volume":"18","author":"F Fabris","year":"2017","journal-title":"Biogerontology"},{"issue":"1","key":"pcbi.1008429.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-19333-x","article-title":"Gene Annotation Bias Impedes Biomedical Research","volume":"8","author":"WA Haynes","year":"2018","journal-title":"Scientific Reports"},{"issue":"1","key":"pcbi.1008429.ref010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"J Friedman","year":"2010","journal-title":"Journal of Statistical Software"},{"issue":"9","key":"pcbi.1008429.ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"Kernlab\u2014An S4 Package for Kernel Methods in R","volume":"11","author":"A Karatzoglou","year":"2004","journal-title":"Journal of Statistical Software"},{"key":"pcbi.1008429.ref012","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, et al. 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