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dc.contributor.authorPahadia, M.
dc.contributor.authorSrivastava, A.
dc.contributor.authorSrivastava, D.
dc.contributor.authorPatil, N.
dc.identifier.citationInternational Conference on Computing, Communication and Automation, ICCCA 2015, 2015, Vol., , pp.678-682en_US
dc.description.abstractCounting 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.titleClassification of multi-genomic data using MapReduce paradigmen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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