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

Authors:
Gong-Xin Yu;George Ostrouchov;Al Geist;Nagiza F. Samatova
Affiliations:
-;-;-;-
Venue:
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Year:
2003

Citing 1
Cited 9

Using the Fisher Kernel Method to Detect Remote Protein Homologies

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology

Credit scoring with a data mining approach based on support vector machines

Expert Systems with Applications: An International Journal
Prediction model building and feature selection with support vector machines in breast cancer diagnosis

Expert Systems with Applications: An International Journal
Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

Expert Systems with Applications: An International Journal
A Hybrid Documents Classification Based on SVM and Rough Sets

AST '09 Proceedings of the 2009 International e-Conference on Advanced Science and Technology
Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

Expert Systems with Applications: An International Journal
Combination of rough sets and genetic algorithms for text classification

AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Local fractal and multifractal features for volumic texture characterization

Pattern Recognition
Asset portfolio optimization using support vector machines and real-coded genetic algorithm

Journal of Global Optimization
A loan default discrimination model using cost-sensitive support vector machine improved by PSO

Information Technology and Management

Quantified Score

Hi-index	0.02

Visualization

Abstract

This paper presents a novel algorithm for identificationand functional characterization of "key" genomefeatures responsible for a particular biochemical processof interest. The central idea is that individual genomefeatures are identified as "key" features if thediscrimination accuracy between two classes of genomeswith respect to a given biochemical process is sufficientlyaffected by the inclusion or exclusion of these features. Inthis paper, genome features are defined by high-resolutiongene functions. The discrimination procedureutilizes the Support Vector Machine classificationtechnique. The application to the oxygenic photosyntheticprocess resulted in 126 highly confident candidategenome features. While many of these features are well-knowncomponents in the oxygenic photosyntheticprocess, others are completely unknown, even includingsome hypothetical proteins. It is obvious that ouralgorithm is capable of discovering features related to atargeted biochemical process.