Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques
Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques
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  • Lodhi (computing, Imperial College London, UK) and Yamanishi (Kyoto University, Japan) compile 17 chapters of current research in machine learning and applications to chemoinformatics tasks to study and solve complex chemical problems using computational tools and methods. Researchers in computational biology, chemoinformatics and chemometrics, computer science, and other scientific and computer-related fields in Europe, the US, and Japan discuss methods for computing similarity in chemical spaces; methodologies that combine graph mining and machine learning techniques; important elements of chemoinformatics, including statistical and Bayesian techniques and partial least square methods; the integration of ideas from fields like chemoinformatics, bioinformatics, and systems biology for the development of methods to solving chemical problems; and machine learning for chemical genomics that requires useful computational approaches to investigate the relationship between chemical space of possible compounds and genomic space of possible genes or proteins. Intended audiences are researchers, scientists, and specialists in fields ranging from chemistry and biology to machine learning, and those at pharmaceutical, agrochemical, and biotechnology companies. Annotation c2010 Book News, Inc., Portland, OR (booknews.com)
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