Abstract
The results of high-throughput experiments consist of numerous candidate genes, proteins, or other molecules potentially associated with diseases. A challenge for omics science is the knowledge extraction from the results and the filtering of promising gene or protein candidates. Especially, the hot topic in clinical scenarios consists of highlighting the behavior of few molecules related to some specific disease. In this contest, different computational approaches, also referred Gene prioritization methods, ensure to identify the most related genes to a disease among a larger set of candidate genes. The identification requires the use of domain-specific knowledge that is often encoded into ontologies.
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