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DC Field | Value | Language |
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dc.contributor.author | Pahadia, M. | |
dc.contributor.author | Srivastava, A. | |
dc.contributor.author | Srivastava, D. | |
dc.contributor.author | Patil, N. | |
dc.date.accessioned | 2020-03-30T10:03:11Z | - |
dc.date.available | 2020-03-30T10:03:11Z | - |
dc.date.issued | 2015 | |
dc.identifier.citation | International Conference on Computing, Communication and Automation, ICCCA 2015, 2015, Vol., , pp.678-682 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/7957 | - |
dc.description.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. | en_US |
dc.title | Classification of multi-genomic data using MapReduce paradigm | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
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