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Graph Classification Methods in Chemoinformatics

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

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Abstract

Graphs are general and powerful data structures that can be used to represent diverse kinds of molecular objects such as chemical compounds, proteins, and RNAs. In recent years, computational analysis of tens of thousands of labeled graphs has become possible by advanced graph mining methods. For example, frequent pattern mining methods such as gSpan can enumerate all frequent subgraphs in a graph database efficiently. This chapter reviews basics of graph mining methodology and its application to chemoinformatics and bioinformatics. Graph classification and regression techniques based on subgraph patterns are also reviewed extensively.

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Author information

Authors and Affiliations

  1. AIST Computational Biology Research Center Tokyo, Tokyo, Japan

    Koji Tsuda

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  1. Koji Tsuda
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Correspondence to Koji Tsuda .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Tsuda, K. (2011). Graph Classification Methods in Chemoinformatics. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_16

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