Comparison of Machine Learning Algorithms in Recognation of Regulatory Region of DNA

Comparison of Machine Learning Algorithms in Recognation of Regulatory Region of DNA

Gunay, Karli

Keywords: Data mining, machine learning, supervised learning, classification, rule-based algorithms. Abstract Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from very large real world database. These databases contain potential gold mine of valuable information, but it is beyond human ability to analyze massive amount of data and elicit meaningful patterns by using conventional techniques. In this study, DNA sequence was analyzed to locate promoter which is a regulatory region of DNA located upstream of a gene, providing a control point for regulated gene transcription. In this study, some supervised learning algorithms such as artificial neural network (ANN), RULES-3 and newly developed keREM-IREM rule induction algorithms were used to analyse to DNA sequence. In the experiments different option of keREM, RULES-3 and ANN were used, and according to the empirical comparisons, the algorithms appeared to be comparable to well-known algorithms in terms of the accuracy of the extracted rule in classifying unseen data.

Conference or Workshop Item
PeerReviewed

2012-05-31

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