ArticlePLoS computational biology2020
Identifying longevity associated genes by integrating gene expression and curated annotations.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Paramylon isolated from Euglena gracilis EOD-1 extends lifespan through activation of DAF-16-mediated antioxidant pathway via clec-196 in Caenorhabditis elegans.Scientific reports · 2025Article
- Uncharacterized yeast geneeLife · 2024Article
- Human Ageing Genomic Resources: updates on key databases in ageing research.Nucleic acids research · 2024Article
- Replicative aging in yeast involves dynamic intron retention patterns associated with mRNA processing/export and protein ubiquitination.Microbial cell (Graz, Austria) · 2024Article
- Article
- PolyQ length co-evolution in neural proteins.NAR genomics and bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.