Recent advances in predicting gene-disease associations

F1000Research
Kenneth Opap, Nicola Mulder

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.

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Software Mentioned

tmChem
Exomiser
tmVar
PubTator
SimConcept
GeneRank
SR4GN
Implicitome
DisGeNET
GNorm

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