1996-2019. All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


 Online Journal of Bioinformatics  

  Volume 15 (3): 243-252, 2014.

Data mining techniques for biological sequence classification


Anubha Dubey PhD.


Department of Bioinformatics,MANIT, BHOPAL (M.P)




Dubey A., Data mining techniques for biological sequence classification, Onl J Bioinform., 15 (3): 243-252, 2014. The voluminous amount of gene data, microarrays, nucleotides and peptide sequences of bacteria, fungi, virus and other organisms generate useful information about disease processes. Because wet lab sequence analysis is time consuming and expensive, there is a need to develop data mining techniques and machine learning models to extract information from data. Usually classification is a preliminary step for examining a set of cases that can be grouped based on similarity to each other. Data mining techniques/tools for bio molecular sequences and data classification such as WEKA, SVM, Fuzzy-sets and others are described


Keywords: WEKA, SVM, Fuzzy-set, Classification, Sequence, Microarray.