Abstract
Candidate gene prioritization is the process of identifying and ranking new genes as potential candidates of being associated with a disease or phenotype. Integrating multiple sources of biological knowledge for gene prioritization can improve performance. We developed a novel network-based gene prioritization algorithm called Knowledge Network Gene Prioritization (KNGP) that can incorporate node weights in addition to the usually used link weights. The online Web implementation of KNGP can handle small input files while the downloadable R software package can handle larger input files. We also provide several files of coded biological knowledge that can be used by KNGP.
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