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Review
. 2017 Apr 26:6:578.
doi: 10.12688/f1000research.10788.1. eCollection 2017.

Recent advances in predicting gene-disease associations

Kenneth Opap  1 , Nicola Mulder  1
Affiliations
Review

Recent advances in predicting gene-disease associations

Kenneth Opap et al. F1000Res. .

Abstract

Deciphering gene-disease association is a crucial step in designing therapeutic strategies against diseases. There are experimental methods for identifying gene-disease associations, such as genome-wide association studies and linkage analysis, but these can be expensive and time consuming. As a result, various in silico methods for predicting associations from these and other data have been developed using different approaches. In this article, we review some of the recent approaches to the computational prediction of gene-disease association. We look at recent advancements in algorithms, categorising them into those based on genome variation, networks, text mining, and crowdsourcing. We also look at some of the challenges faced in the computational prediction of gene-disease associations.

Keywords: GWAS; Genome Wide Association Studies; computational prediction; gene-disease association; linkage anaylsis.

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Conflict of interest statement

Competing interests: No competing interests were disclosed.No competing interests were disclosed.No competing interests were disclosed.

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