Publication Type
Journal Article
Version
acceptedVersion
Publication Date
7-2021
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 and complementary 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 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.
Keywords
Disease gene prediction, Network-based methods, Graph representation learning
Discipline
Medicine and Health Sciences | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Briefings in Bioinformatics
Volume
22
Issue
4
First Page
1
Last Page
15
ISSN
1467-5463
Identifier
10.1093/bib/bbaa303
Publisher
Oxford University Press
Embargo Period
4-25-2021
Citation
ATA, Sezin Kircali; WU, Min; FANG, Yuan; LE, Ou-Yang; KWOH, Chee Keong; and LI, Xiao-Li.
Recent advances in network-based methods for disease gene prediction. (2021). Briefings in Bioinformatics. 22, (4), 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/5901
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1093/bib/bbaa303
Included in
Medicine and Health Sciences Commons, Numerical Analysis and Scientific Computing Commons