Gene Prioritization Tools

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Abstract

Omics sciences are widely used to analyze diseases at a molecular level. Usually, results of omics experiments are sets of candidate genes potentially involved in different diseases. The interpretation of results and the filtering of candidate genes or proteins selected in an experiment is a challenge in some scenarios. To filter out false positive genes, different approaches for selecting genes have been introduced. Such approaches are often referred to as Gene prioritization methods. They aim to identify the most related genes to a disease among a larger set of candidates genes, through the use of computational methods.

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Marianna Milano received the Laurea degree in biomedical engineering from the University Magna Græcia of Catanzaro, Italy, in 2011. She is a Ph.D student at the University Magna Græcia of Catanzaro. Her main research interests are on biological data analysis and seman- tic-based analysis of biological data. She is a member of IEEE Computer Society.

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