Abstract
Abstract: Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.
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Acknowledgements
The authors would like to sincerely thank the anonymous reviewers for their valuable comments and stimulus which were used to improve this final version. We also acknowledge all support from the Data Science Research Unit (DSRU) at the Charles Sturt University, Australia.
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Rahman, A., Nimmy, S.F., Sarowar, G. (2019). Developing an Automated Machine Learning Approach to Test Discontinuity in DNA for Detecting Tuberculosis. In: Xu, J., Cooke, F., Gen, M., Ahmed, S. (eds) Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ICMSEM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93351-1_23
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