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Chapitre D'ouvrage Année : 2015

Graph kernels in chemoinformatics

Résumé

Graphs provide a generic data structure widely used in chemo and bioin- formatics to represent complex structures such as chemical compounds or complex interactions between proteins. However, the high flexibility of this data structure does not allow to readily combine it with usual machine learn- ing algorithms based on a vectorial representation of input data. Graph kernels are defined as similarity measures between graphs. Under mild conditions, graph kernels correspond to scalar products between possi- bly implicit graph embeddings into an Hilbert space. Thanks to this graph embedding, machine learning methods which may be rewritten so as to use only scalar products between input data, such as SVM, can be applied on graphs. Graph kernels thus provide a natural connection between graph space and machine learning.
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Dates et versions

hal-01201933 , version 1 (18-09-2015)

Identifiants

  • HAL Id : hal-01201933 , version 1

Citer

Benoit Gaüzère, Luc Brun, Didier Villemin. Graph kernels in chemoinformatics. Matthias Dehmer and Frank Emmert-Streib. Quantitative Graph Theory Mathematical Foundations and Applications, CRC Press, pp.425-470, 2015, 978-1-4665-8451-8. ⟨hal-01201933⟩
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