Ranking Methods of Candidates for p53 Inhibitors Considering Cytotoxicity Using Machine Learning

Haruka Motohashi, Tatsuro Teraoka, Shin Aoki, Hayato Ohwada

研究成果: Conference contribution査読

抄録

In this paper, we propose new methods to make the rankings of candidates for p53 inhibitors, considering both radioprotective function and cytotoxicity. We use features about compound's structure including fingerprints, machine learning methods and ranking methods. As a result, we presented the regression models of cytotoxicity and radioprotective function of them to determine the rankings.

本文言語English
ホスト出版物のタイトルACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
出版社Association for Computing Machinery, Inc
ページ数1
ISBN(電子版)9781450357944
DOI
出版ステータスPublished - 15 8月 2018
イベント9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 - Washington, United States
継続期間: 29 8月 20181 9月 2018

出版物シリーズ

名前ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Conference

Conference9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018
国/地域United States
CityWashington
Period29/08/181/09/18

フィンガープリント

「Ranking Methods of Candidates for p53 Inhibitors Considering Cytotoxicity Using Machine Learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル