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Prediction of Compound-protein Interactions with Machine Learning Methods

Yoshihiro Yamanishi Hisashi Kashima
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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.

Penulis (2)

Y

Yoshihiro Yamanishi

H

Hisashi Kashima

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Yamanishi, Y., Kashima, H. (2012). Prediction of Compound-protein Interactions with Machine Learning Methods. https://doi.org/10.4018/978-1-60960-818-7.ch315

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Informasi Jurnal
Tahun Terbit
2012
Bahasa
en
Sumber Database
CrossRef
DOI
10.4018/978-1-60960-818-7.ch315
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Terbatas