Graph kernels for chemoinformatics

Hisashi Kashima, Hiroto Saigo, Masahiro Hattori, Koji Tsuda

研究成果: Chapter in Book/Report/Conference proceedingChapter

8 被引用数 (Scopus)

抄録

The authors review graph kernels which is one of the state-of-the-art approaches using machine learning techniques for computational predictive modeling in chemoinformatics. The authors introduce a random walk graph kernel that defines a similarity between arbitrary two labeled graphs based on label sequences generated by random walks on the graphs. They introduce two applications of the graph kernels, the prediction of properties of chemical compounds and prediction of missing enzymes in metabolic networks. In the latter application, the authors propose to use the random walk graph kernel to compare arbitrary two chemical reactions, and apply it to plant secondary metabolism.

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1-15
ページ数15
ISBN(印刷版)9781615209118
DOI
出版ステータス出版済み - 12 1 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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