Abstract
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.
Original language | English |
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Title of host publication | Chemoinformatics and Advanced Machine Learning Perspectives |
Subtitle of host publication | Complex Computational Methods and Collaborative Techniques |
Publisher | IGI Global |
Pages | 304-317 |
Number of pages | 14 |
ISBN (Print) | 9781615209118 |
DOIs | |
Publication status | Published - 2010 |
All Science Journal Classification (ASJC) codes
- Agricultural and Biological Sciences(all)