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|dc.identifier.citation||International Conference on Computing, Communication and Automation, ICCCA 2015, 2015, Vol., , pp.678-682||en_US|
|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|
|Appears in Collections:||2. Conference Papers|
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