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How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars

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A large number of different machine learning methods can potentially be used for ligand-based virtual screening. In our contribution, we focus on three specific nonlinear methods, namely support vector regression, Gaussian process models, and decision trees. For each of these methods, we provide a short and intuitive introduction. In particular, we will also discuss how confidence estimates (error bars) can be obtained from these methods. We continue with important aspects for model building and evaluation, such as methodologies for model selection, evaluation, performance criteria, and how the quality of error bar estimates can be verified. Besides an introduction to the respective methods, we will also point to available implementations, and discuss important issues for the practical application.
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Keywords: Gaussian process; Machine learning; decision tree; error bars; model building; parameter estimation; support vector machine

Document Type: Research Article

Affiliations: Technische Universit├Ąt Berlin, Department of Computer Science, Franklinstra├če 28/29, D-10587 Berlin, Germany.

Publication date: June 1, 2009

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  • Combinatorial Chemistry & High Throughput Screening publishes full length original research articles and reviews describing various topics in combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) and/or high throughput screening (e.g. developmental, practical or theoretical). Ancillary subjects of key importance, such as robotics and informatics, will also be covered by the journal. In these respective subject areas, Combinatorial Chemistry & High Throughput Screening is intended to function as the most comprehensive and up-to-date medium available. The journal should be of value to individuals engaged in the process of drug discoveryand development, in the settings of industry, academia or government.
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