![](/jspui/image/dspace_header.jpg)
Please use this identifier to cite or link to this item:
https://idr.nitk.ac.in/jspui/handle/123456789/7957
Title: | Classification of multi-genomic data using MapReduce paradigm |
Authors: | Pahadia, M. Srivastava, A. Srivastava, D. Patil, N. |
Issue Date: | 2015 |
Citation: | International Conference on Computing, Communication and Automation, ICCCA 2015, 2015, Vol., , pp.678-682 |
Abstract: | Counting the number of occurences of a substring in a string is a problem in many applications. This paper suggests a fast and efficient solution for the field of bioinformatics. A k-mer is a k-length substring of a biological sequence. k-mer counting is defined as counting the number of occurences of all the possible k-mers in a biological sequence. k-mer counting has uses in applications ranging from error correction of sequencing reads, genome assembly, disease prediction and feature extraction. We provide a Hadoop based solution to solve the k-mer counting problem and then use this for classification of multi-genomic data. The classification is done using classifiers like Naive Bayes, Decision Tree and Support Vector Machine(SVM). Accuracy of more than 99% is observed. � 2015 IEEE. |
URI: | http://idr.nitk.ac.in/jspui/handle/123456789/7957 |
Appears in Collections: | 2. Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.