Graph kernels for chemoinformatics

Hisashi Kashima, Hiroto Saigo, Masahiro Hattori, Koji Tsuda

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationChemoinformatics and Advanced Machine Learning Perspectives
Subtitle of host publicationComplex Computational Methods and Collaborative Techniques
PublisherIGI Global
Pages1-15
Number of pages15
ISBN (Print)9781615209118
DOIs
Publication statusPublished - Dec 1 2010
Externally publishedYes

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Agricultural and Biological Sciences(all)

Cite this

Kashima, H., Saigo, H., Hattori, M., & Tsuda, K. (2010). Graph kernels for chemoinformatics. In Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques (pp. 1-15). IGI Global. https://doi.org/10.4018/978-1-61520-911-8.ch001