Prediction of compound-protein interactions with machine learning methods

Yoshihiro Yamanishi, Hisashi Kashima

研究成果: 書籍/レポート タイプへの寄稿

抄録

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

本文言語英語
ホスト出版物のタイトルChemoinformatics and Advanced Machine Learning Perspectives
ホスト出版物のサブタイトルComplex Computational Methods and Collaborative Techniques
出版社IGI Global
ページ304-317
ページ数14
ISBN(印刷版)9781615209118
DOI
出版ステータス出版済み - 2010
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 農業および生物科学(全般)

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