title: Similarity-based disease risk assessment for personal genomes: proof of concept.
abstract: The increasing availability of personal genome data has led to escalating needs by consumers to understand the implications of their gene sequences. At present, poorly integrated genetic knowledge has not met these needs. This proof-of-concept study proposes a similarity-based approach to assess the disease risk predisposition for personal genomes. We hypothesize that the semantic similarity between a personal genome and a disease can indicate the disease risks in the person. We developed a knowledge network that integrates existing knowledge of genes, diseases, and symptoms from six sources using the Semantic Web standard, Resource Description Framework (RDF). We then used latent relationships between genes and diseases derived from our knowledge network to measure the semantic similarity between a personal genome and a genetic disease. For demonstration, we showed the feasibility of assessing the disease risks in one personal genome and discussed related methodology issues.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
authors.forename: Jung Hoon
authors.initials: JH
authors.lastname: Woo
authors.forename: Albert M
authors.initials: AM
authors.lastname: Lai
authors.forename: Wendy K
authors.initials: WK
authors.lastname: Chung
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D005260:Female; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D020407:Internet; D051188:Knowledge Bases; D008297:Male; D010051:Ovarian Neoplasms / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D011471:Prostatic Neoplasms / Q000235:genetics; D018570:Risk Assessment / Q000379:methods*; D012660:Semantics