Combining Pareto-Optimal Clusters using Supervised Learning for Identifying Co-expressed Genes (Accepted in BMC Bioinformatics) Ujjwal Maulik Department of Computer Science & Engineering, Jadavpur University, Kolkata 700032, India, drumaulik@cse.jdvu.ac.in Anirban Mukhopadhyay Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, India, anirban@klyuniv.ac.in Sanghamitra Bandyopadhyay Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, India, sanghami@isical.ac.in

Abstract Data Sets Code

CODE MOGA-SVM has been implemented using MATLAB. The code is available in zipped form here. For SVM classification, it uses multiclass SVM light implementation. The executables for SVM light are also included in the zip file for convenience. For the instructions to execute MOGA-SVM, users are requested to first read the file Readme.txt included in the zip file. Use of MOGA-SVM is free as long as it is used for any academic and non-commercial purpose. If you use MOGA-SVM, please cite the following reference: U. Maulik, A. Mukhopadhyay and S. Bandyopadhyay, “Combining Pareto-Optimal Clusters using Supervised Learning for Identifying Co-expressed Genes”, submitted to BMC Bioinformatics (2008). For any query regarding MOGA-SVM, please mail to anirban@klyuniv.ac.in