A sequence-based approach for identifying recombination spots in Saccharomyces cerevisiae by using hyper-parameter optimization in FastText and support vector machine

• Published in: Chemometrics and intelligent laboratory systems 2019-11, Vol.194, p.103855, Article 103855
• Main Authors: Do, Duyen Thi, Le, Nguyen Quoc Khanh
• Format: Article
• Language: English
• Subjects: Continuous bag of words; DNA sequencing; FastText; Meiotic recombination; Prediction model; Support vector machine
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2019.103855

Meiotic recombination is a biological process which plays a crucial role in genetic evolution. Therefore, the ability of machine learning models in extracting desire information embedded in DNA sequences has drawn a great deal of attention among biologists. Recently, several attempts have been made to address this problem, however, the performance results still need to be improved. The current study aims to investigate the relationship between natural language processing model and supervised learning in classifying DNA sequences. The idea is to treat DNA sequences by FastText model, including sub-word information and then use them as features in a suitable supervised learning algorithm. To the end, this hybrid approach helps us classify DNA recombination spots with achieved sensitivity of 90%, specificity of 94.76%, accuracy of 92.6%, and MCC of 0.851. These results have suggested that our newly proposed method is superior to other methods on the same benchmark dataset. This study, therefore, could shed the light on developing the prediction models for recombination spots in particular, and DNA sequences in general. •A computational method for classifying recombination spots in Saccharomyces cerevisiae with high performance.•Features are extracted from FastText algorithm with sub-word information.•Optimizing hyper-parameters for support vector machine algorithm.•Compared with the state-of-the-art methods, our method had a significant improvement in all of the measurement metrics.•A basis for further research that can improve the predictive performance of DNA sequencing problems.