Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA)
巻, 号, ページ
pp. 129-134
出版年月
2019年7月29日
出版者
和文:
英文:
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
会議名称
和文:
英文:
The 25th Int'l Conf on Parallel and Distributed Processing Techniques and Applications(PDPTA2019) CSCE'19
開催地
和文:
英文:
Las Vegas
アブストラクト
Homology modeling is a protein structure prediction method and practically useful because the accuracy of generated models is often high if we can find a good template structure. The method constructs a structure model based on a template structure and a sequence alignment between a query protein sequence and a protein sequence of the template protein. Thus, the quality of the sequence alignment is crucial for the prediction. Recently, a novel method to improve the sequence alignment using supervised machine learning technique was proposed. The method showed better accuracy compared with previous methods but required huge computation. Because almost millions of machine learning predictions are needed for a sequence alignment generation of a pair of protein sequences. Thus, in this study, we proposed a novel method to accelerate the sequence generation by optimizing the algorithm and parameters of machine learning predictions. As results, proposed method was approximately 21-times faster than the original one in exchange of trivial decrease in accuracy.
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