Computing Gene Functional Similarity Using Combined Graphs

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dc.contributor.authorBettayeb, Said
dc.date.accessioned2020-05-12T17:31:43Z
dc.date.available2020-05-12T17:31:43Z
dc.date.issued2012
dc.description.abstractThe Gene Ontology has been used extensively for measuring the functional similarity among genes of various organisms. All the existing gene similarity methods use either molecular function or biological process taxonomies in computing gene similarity. In this paper, we apply an algorithm for combining graphs to connect the molecular function (F) and biological process (P) taxonomies into one FP taxonomy graph. We then measure the functional similarity of two genes using the resulting FP graph with path length function. The two aspects of GO, molecular function and biological process, are combined by connecting F nodes with P nodes using gene ontology annotation, GOA, databases. By combining two GO graphs, we can have more comprehensive way to explore the functional relationships between genes. We conducted the evaluation on a dataset of OMIM disease phenotypes to estimate the similarity of disease proteins from various diseases.en_US
dc.identifier.citationComputing Gene Functional Similarity Using Combined Graphs, (With H. Al-Mubaid and Anurag Nagar), Proc. of the Symposium on Applied Computing (SAC 2012), March 26-30, 2012, Riva del Garda (Trento), Italyen_US
dc.identifier.urihttps://hdl.handle.net/10657.1/2336
dc.publisherSymposium on Applied Computingen_US
dc.subjectApplied computing Life and medical sciences Health care information systems Information systems Information storage systems Record storage systemsen_US
dc.titleComputing Gene Functional Similarity Using Combined Graphsen_US
dc.typePresentationen_US

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Said Bettayeb