Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value

摘要

The advent of next-generation sequencing technologies has rapidly decreased the cost of sequencing genomes and increased the throughput exponentially, making it possible to amass large amounts of data for conducting genome-wide association studies (GWAS) [1, 2]. Despite the abundance of high resolution genomic data, traditional GWAS approaches have faced mathematical and computational challenges in identifying candidate genes in diseases with complex genetic etiology.