Computational Systems Bioinformatics Conference, International IEEE Computer Society (2003)
Aug. 11, 2003 to Aug. 14, 2003
Gong-Xin Yu , Oak Ridge National Laboratory
George Ostrouchov , Oak Ridge National Laboratory
Al Geist , Oak Ridge National Laboratory
Nagiza F. Samatova , Oak Ridge National Laboratory
This paper presents a novel algorithm for identification and functional characterization of "key" genome features responsible for a particular biochemical process of interest. The central idea is that individual genome features are identified as "key" features if the discrimination accuracy between two classes of genomes with respect to a given biochemical process is sufficiently affected by the inclusion or exclusion of these features. In this paper, genome features are defined by high-resolution gene functions. The discrimination procedure utilizes the Support Vector Machine classification technique. The application to the oxygenic photosynthetic process resulted in 126 highly confident candidate genome features. While many of these features are well-known components in the oxygenic photosynthetic process, others are completely unknown, even including some hypothetical proteins. It is obvious that our algorithm is capable of discovering features related to a targeted biochemical process.
key genome features, oxygenic photosynthetic process, genome comparative analysis, support vector machines
A. Geist, G. Yu, G. Ostrouchov and N. F. Samatova, "An SVM-based Algorithm for Identification of Photosynthesis-specific Genome Features," Computational Systems Bioinformatics Conference, International IEEE Computer Society(CSB), Stanford, California, 2003, pp. 235.