NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.

ABSTRACT:

Motivation

Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning (ML) setting in which only a subset of instances are labeled as positive while the rest of the dataset is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery.

Results

The performances of the new labeling algor

SUBMITTER: Stolfi P 

PROVIDER: S-EPMC9933847 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature