Dimension reduction for microarray data using multi-objective ant colony optimisation
Saroj Ratnoo, Jyoti Ahuja · International Journal of Computational Systems Engineering · 2017
DNA microarray technology is a nascent technology having vast applications in the molecular biology. Despite of its many useful applications in disease diagnosis and drug discovery, analysing DNA microarray data has become a challenge for bio-analysts. Microarrays present curse of dimensionality problem and call for the development of new techniques to handle it. The problem of dimensionality reduction or feature/gene selection has been posed as a multi-objective problem in the literature and can be better solved using multi-objective meta-heuristics. In this paper, we have proposed a multi-objective ant colony optimisation (MOACO) algorithm for gene selection. The contribution of this paper is to obtain multiple non-dominated solutions instead of a single best solution. These multiple solutions enable a user to select a solution according to his/her preference or application domain. The predicted genes also called as bio-markers, can help in disease diagnosis and may direct the progress of drug efficacy. A comparative analysis of the proposed method has been done using some conventional feature selection techniques and a method based on multi-objective particle swarm optimisation (MOPSO) proposed by Mukhopadhyay and Mandal (2014). The results obtained confirm the superiority of MOACO-based approach over the others in terms of various performance metrics.