Recent Advances in Network-based Methods for Disease Gene Prediction
- URL: http://arxiv.org/abs/2007.10848v2
- Date: Thu, 17 Dec 2020 22:47:07 GMT
- Title: Recent Advances in Network-based Methods for Disease Gene Prediction
- Authors: Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh and
Xiao-Li Li
- Abstract summary: Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers.
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.
- Score: 15.625526953844638
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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