Table of Contents
ISRN Computational Biology
Volume 2013 (2013), Article ID 159135, 5 pages
http://dx.doi.org/10.1155/2013/159135
Review Article

Text Mining Perspectives in Microarray Data Mining

Data and Text Mining Laboratory, Deptartment of Bioinformatics, Bharathiar University, Coimbatore 641 046, India

Received 19 July 2013; Accepted 4 September 2013

Academic Editors: Z. Su and Z. Yu

Copyright © 2013 Jeyakumar Natarajan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.