Analysis of Kernel Based Protein Classification Strategies Using Pairwise Sequence Alignment Measures

Authors:
Dino Franklin;Somdutta Dhir;Sándor Pongor
Affiliations:
Federal University of Goiás, Catalão GO, Brazil 75705-220;International Centre for Genetic Engineering and Biotechnology, Trieste, Italy 34012;International Centre for Genetic Engineering and Biotechnology, Trieste, Italy 34012
Venue:
Computational Intelligence Methods for Bioinformatics and Biostatistics
Year:
2009

Citing 4
Cited 0

Core Vector Machines: Fast SVM Training on Very Large Data Sets

The Journal of Machine Learning Research
ROCR: visualizing classifier performance in R

Bioinformatics
Application of a simple likelihood ratio approximant to protein sequence classification

Bioinformatics
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We evaluated methods of protein classification that use kernels built from BLAST output parameters. Protein sequences were represented as vectors of parameters (e.g. similarity scores) determined with respect to a reference set, and used in Support Vector Machines (SVM) as well as in simple nearest neighbor (1NN) classification. We found, using ROC analysis, that aggregate representations that use aggregate similarities with respect to a few object classes, were as accurate as the full vectorial representations, and that a jury of 6 1NN-based aggregate classifiers performed as well as the best SVM classifiers, while they required much less computational time.