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 pattern recognition terminology, PFR is a multiclass classification problem to be solved by employing feature analysis and pattern classification techniques. This work reformulates PFR into a multiobjective optimization problem and proposes a multiobjective 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 existing results. In addition, MOFASA identifies relevant features for further biological analysis.