An SVM-based Algorithm for Identification of Photosynthesis-specific Genome Features

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 behind our algorithm is that individual genome features (or their combinations) are identified as significant “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 (SVM) classification technique. Changes in classification accuracy in response to addition or deletion of genome features measure the significance of these features. The application of this SVM-based feature identification algorithm to the oxygenic photosynthetic process resulted in a total of 126 highly confident candidate genome features. They co...

• Type: Conference
• Year: 2003
• Where: CSB
• Authors: Gong-Xin Yu, George Ostrouchov, Al Geist, Nagiza F. Samatova
• Keywords: Biochemical Process, CSB 2003, Dominant Genome Features, Genome Features