

Author: Denaxas Spiridon C. Tjortjis Christos
Publisher: Inderscience Publishers
ISSN: 1748-5673
Source: International Journal of Data Mining and Bioinformatics, Vol.2, Iss.3, 2008-09, pp. : 216-235
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
We propose an approach for quantifying the biological relatedness between gene products, based on their properties, and measure their similarities using exclusively statistical NLP techniques and Gene Ontology (GO) annotations. We also present a novel similarity figure of merit, based on the vector space model, which assesses gene expression analysis results and scores gene product clusters' biological coherency, making sole use of their annotation terms and textual descriptions. We define query profiles which rapidly detect a gene product cluster's dominant biological properties. Experimental results validate our approach, and illustrate a strong correlation between our coherency score and gene expression patterns.
Related content






LEARNING WITH GENE ONTOLOGY ANNOTATION USING FEATURE SELECTION AND CONSTRUCTION
By Akand Elma
Applied Artificial Intelligence, Vol. 24, Iss. 1-2, 2010-01 ,pp. :




Gene clusters as intersections of powers of paths
By Costa Vítor
Journal of the Brazilian Computer Society, Vol. 18, Iss. 2, 2012-06 ,pp. :