  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.
