A novel ACO–GA hybrid algorithm for feature selection in protein function prediction |
| |
Authors: | Shahla Nemati Mohammad Ehsan Basiri Nasser Ghasem-Aghaee Mehdi Hosseinzadeh Aghdam |
| |
Affiliation: | aYoung Research Club, Islamic Azad University, Arsenjan Branch, Fars, Iran;bComputer Engineering Department, University of Isfahan, Hezar Jerib Avenue, Isfahan, Iran;cComputer Engineering Department, Technical & Engineering Faculty of Bonab, University of Tabriz, Tabriz, Iran |
| |
Abstract: | Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm. |
| |
Keywords: | Protein function prediction Ant colony optimization (ACO) Genetic algorithm (GA) Feature selection (FS) Hierarchical classification Bioinformatics |
本文献已被 ScienceDirect 等数据库收录! |
|