title: Computational approaches for human disease gene prediction and ranking.
abstract: While candidate gene association studies continue to be the most practical and frequently employed approach in disease gene investigation for complex disorders, selecting suitable genes to test is a challenge. There are several computational approaches available for selecting and prioritizing disease candidate genes. A majority of these tools are based on guilt-by-association principle where novel disease candidate genes are identified and prioritized based on either functional or topological similarity to known disease genes. In this chapter we review the prioritization criteria and the algorithms along with some use cases that demonstrate how these tools can be used for identifying and ranking human disease candidate genes.
authors.affiliation: Department of Computer Science, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA.
authors.forename: Cheng
authors.initials: C
authors.lastname: Zhu
authors.forename: Chao
authors.initials: C
authors.lastname: Wu
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Aronow
authors.forename: Anil G
authors.initials: AG
authors.lastname: Jegga
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D005826:Genetics, Medical / Q000379:methods* / Q000639:trends; D006801:Humans