Multi-Class Protein Fold Recognition Using Multi-Objective Evolutionary Algorithms
Abstract
Protein fold recognition (PFR) is an important approach to structure discovery without relying on sequence similarity. In the pattern recognition terminology, PFR is a multi-class classification problem to be solved by employing feature analysis and pattern classification techniques. This paper reformulates PFR into a multi-objective optimization problem (7) and proposes a Multi-Objective Feature Analysis and Selection Algorithm (MOFASA). We use support vector machines as the classifier. Experimental results on the Structural Classification of Protein (SCOP) data set indicate that MOFASA is capable of achieving comparable performances to the results reported in (10). In addition, MOFASA identifies relevant features for further biological analysis.
Authors: Stanley Y. M. Shi, Ponnuthurai Nagaratnam Suganthan, Kalyanmoy Deb
Published in: Computational Intelligence in Bioinformatics and Computational Biology (2004)