Jayakishan Meher

Work place: Computer Science and Engg, Vikash College of Engineering for Women, Bargarh, Odisha, India

E-mail: jk_meher@yahoo.co.in


Research Interests: Medicine & Healthcare


Jayakishan Meher: received his Ph.D from Sambalpur University,  M.Tech in Computer Science & Engg from J.R.N RV University and M.Tech in Electronics and Telecommunication Engineering from Veer Surendra Sai University of Technology (VSSUT), Burla (formerly known as University College of Engineering), India in 2012, 2007 and 2002 respectively. Currently he is Associate Professor and Head of the department of Computer Science and Engg in Vikash College of Engg for Women, Bargarh, Odisha, India. His research interests include digital signal processing, genome analysis, microarray data analysis, Protein analysis, metal binding, drug design and disease classification and other bioinformatics applications. Recently, he has developed interest in VLSI design for implementation of signal-processing algorithm on bioinformatics applications and also he is tending his research towards more fundamental aspects of plant genomics and proteomics.

Author Articles
Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data

By Jayakishan Meher Ram Chandra Barik Madhab Ranjan Panigrahi Saroj Kumar Pradhan Gananath Dash

DOI: https://doi.org/10.5815/ijitcs.2012.09.10, Pub. Date: 8 Aug. 2012

Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA) with discrete wavelet transform (DWT) to detect informative genes. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.

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