Recent Advances in Network-based Methods for Disease Gene Prediction

TL;DR

This paper reviews recent network-based computational methods for disease gene prediction, categorizes them, and empirically evaluates 14 state-of-the-art approaches across multiple diseases to identify strengths and future directions.

Contribution

It provides a comprehensive, up-to-date survey and empirical analysis of network-based methods for disease gene prediction, highlighting key differences and research gaps.

Findings

Network diffusion methods show strong performance in certain diseases.

Graph representation learning methods outperform traditional machine learning in some cases.

Future research should focus on integrating multi-omics data for improved accuracy.

Abstract

Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false-positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and an up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.