Papers › Positive-Unlabelled Learning for Identifying New Candidate Dietary Restriction-related...
Positive-Unlabelled Learning for Identifying New Candidate Dietary Restriction-related Genes among Ageing-related Genes
Jorge Paz-Ruza, Alex A. Freitas, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas
Dietary Restriction (DR) is one of the most popular anti-ageing interventions, prompting exhaustive research into genes associated with its mechanisms. Recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, existing ML methods naively label genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence; this hinders the reliability of the negative examples (non-DR-related genes) and the method's ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms the existing state-of-the-art non-PU approach for DR-relatedness prediction in three relevant performance metrics. In addition, curation of existing literature finds support for the top-ranked candidate DR-related genes identified by our model.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections