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Positive-unlabelled learning for identifying new candidate dietary restriction-related genes among ageing-related genes

Paz-Ruza, Jorge, Freitas, Alex Alex, A., Alonso-Betanzo, Amparo, Guijarro-Berdinas, Bertha (2024) Positive-unlabelled learning for identifying new candidate dietary restriction-related genes among ageing-related genes. Computers in Biology and Medicine, 180 . Article Number 108999. ISSN 0010-4825. E-ISSN 1879-0534. (doi:10.1016/j.compbiomed.2024.108999) (KAR id:106858)

Abstract

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; 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, the existing ML approach naively labels 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 (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role.

Item Type: Article
DOI/Identification number: 10.1016/j.compbiomed.2024.108999
Uncontrolled keywords: machine learning; data mining; bioinformatics; ageing-related genes; dietary restriction-related genes; positive-unlabelled learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Alex Freitas
Date Deposited: 12 Aug 2024 10:36 UTC
Last Modified: 13 Aug 2024 07:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/106858 (The current URI for this page, for reference purposes)

University of Kent Author Information

Freitas, Alex Alex, A..

Creator's ORCID: https://orcid.org/0000-0001-9825-4700
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