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Title:
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AN ONTOLOGY-DRIVEN SIMILARITY PROVIDING RELIABLE PROTEIN FAMILY RECOGNITION |
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Author(s):
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Fernando Garcia , Francisco J. Lopez , Carlos Cano , Armando Blanco |
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ISBN:
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972-8924-09-7 |
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Editors:
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Nuno Guimarães, Pedro Isaías and Ambrosio Goikoetxea |
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Year:
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2006 |
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Edition:
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Single |
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Keywords:
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Fuzzy Semantic Simililarity, Gene Ontology, Possibilistic. |
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Type:
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Short Paper |
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First Page:
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649 |
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Last Page:
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654 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Last years mapping of the genomes opens the door to understanding the root and causes of many genes behaviour, but the interaction between genes is highly complex. One of the main tasks is to group the genes according to their function. This paper presents a new method for measuring the semantic similarity of genes using the annotations derived from the Gene Ontology (GO). Following the idea of Keller [12], our similarity measure is based in a λ-fuzzy measure [3]. For each gene, it takes into account its whole set of annotations (terms), and for each pair of terms, their nearest common antecessor. The evidence codes of the annotations modify their importance. We computed the weights of the evidence by means of a genetic algorithm (GA). For validating the quality of the measure we took protein families and obtained the similarity for each pair of protein in a similarity matrix. Finally, we applied a possibilistic clustering algorithm and observed how well the families were distinguished. |
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