This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candidates by interactome-based algorithms, but that appear to be involved in/or potentially related to breast cancer, according to a functional analysis based on recent literature.
@article{arxiv.2002.07064,
title = {Biological Random Walks: integrating heterogeneous data in disease gene prioritization},
author = {Michele Gentili and Leonardo Martini and Manuela Petti and Lorenzo Farina and Luca Becchetti},
journal= {arXiv preprint arXiv:2002.07064},
year = {2020}
}