Identifying longevity associated genes by integrating gene expression and curated annotations.

Tytuł:: Identifying longevity associated genes by integrating gene expression and curated annotations.
Autorzy:: Townes FW; Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
Carr K; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Miller JW; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Źródło:: PLoS computational biology [PLoS Comput Biol] 2020 Nov 30; Vol. 16 (11), pp. e1008429. Date of Electronic Publication: 2020 Nov 30 (Print Publication: 2020).
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms:: Gene Expression*
Gene Ontology*
Longevity/*genetics
Algorithms ; Animals ; Caenorhabditis elegans/genetics ; Genes, Fungal ; Machine Learning ; Reproducibility of Results ; Saccharomyces cerevisiae/genetics
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Grant Information:: T32 CA009337 United States CA NCI NIH HHS
Entry Date(s):: Date Created: 20201130 Date Completed: 20210129 Latest Revision: 20210129
Update Code:: 20240105
PubMed Central ID:: PMC7728194
DOI:: 10.1371/journal.pcbi.1008429
PMID:: 33253142
: Czasopismo naukowe

Pełny tekst

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 (C. elegans and S. cerevisiae) 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.
Competing Interests: The authors have declared that no competing interests exist.

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