Predicting radiation protection and toxicity of p53 targeting radioprotectors using machine learning

Masataka Kimura, Shin Aoki, Hayato Ohwada

研究成果: Conference contribution査読

2 被引用数 (Scopus)

抄録

This paper explores machine learning application in the case of drug discovery. We apply extreme gradient boosting and K-nearest neighbor to biomedical data and it signiflcantly outperform former studies using feature selection and proper tuning parameters. The novel application motivated by a recent circumstance that there is a need for rapid development of radio-protectors. It mainly targets the DNA of growing cancer cells, whereas it has adverse side effects, including p53-induced apoptosis of normal tissues and cells. It considered that p53 would be a target for therapeutic and mitigated radioprotection to escape from the apoptotic fate. On the other hand, many types of compounds contain several level of toxicity, so it is important to consider not only radiation protection but also the level of toxicity of candidate compounds for radioprotectors. Compounds of radio-protectors that have low toxicity and high radiation protection are expected. It is possible to do efficiently the compounds discovery using machine learning. This study predicts compounds of radioprotectors using plural machine learning methods, Extreme Gradient Boosting, K-nearest neighbor, SVM and Random Forest. We compare these methods and suggest proper methods to predict radioprotectors.

本文言語English
ホスト出版物のタイトル2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781467389884
DOI
出版ステータスPublished - 4 10月 2017
イベント2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 - Manchester, United Kingdom
継続期間: 23 8月 201725 8月 2017

出版物シリーズ

名前2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017

Conference

Conference2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
国/地域United Kingdom
CityManchester
Period23/08/1725/08/17

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