MOSCFRA: a multi-objective genetic approach for simultaneous clustering and gene ranking

  • Authors:

  • Kartick Chandra Mondal;Anirban Mukhopadhyay;Ujjwal Maulik;Sanghamitra Bandhyapadhyay;Nicolas Pasquier

  • Affiliations:

  • I3S Laboratory, CNRS UMR-6070, University of Nice Sophia-Antipolis, France;Department of Computer Science and Engineering, University of Kalyani, Kalyani, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India;I3S Laboratory, CNRS UMR-6070, University of Nice Sophia-Antipolis, France

  • Venue:
  • CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

Abstract

Microarray experiments generate a large amount of data which is used to discover the genetic background of diseases and to know the characteristics of genes. Clustering the tissue samples according to their co-expressed behavior and characteristics is an important tool for partitioning the dataset. Finding the clusters of a given dataset is a difficult task. This task of clustering is even more difficult when we try to find the rank of each gene, which is known as Gene Ranking, according to their abilities to distinguish different classes of samples. In the literature, many algorithms are available for sample clustering and gene ranking or selection, separately. A few algorithms are also available for simultaneous clustering and feature selection. In this article, we have proposed a new approach for clustering the samples and ranking the genes, simultaneously. A novel encoding technique for the chromosomes is proposed for this purpose and the work is accompleshed using a multi-objective evolutionary technique. Results have been demonstrated for both artificial and real-life gene expression data sets.