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Machine Learning and Applications in Biology

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Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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HelenShin_BioTutorial8.pdf
(Any fulltext), 4MB

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Citation

Shin, H. (2006). Machine Learning and Applications in Biology. Talk presented at 6th Course in Bioinformatics for Molecular Biologists. Bertinoro di Romangna, Italy. 2006-03-18.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D251-2
Abstract
The emergence of the fields of computational biology and bioinformatics has alleviated the burden of solving many biological problems, saving the time and cost required for experiments and also providing predictions that guide new experiments. Within computational biology, machine learning algorithms have played a central role in dealing with the flood of biological data. The goal of this tutorial is to raise awareness and comprehension of machine learning so that biologists can properly match the task at hand to the corresponding analytical approach. We start by categorizing biological problem settings and introduce the general machine learning schemes that fit best to each or these categories. We then explore representative models in further detail, from traditional statistical models to recent kernel models, presenting several up-to-date research projects in bioinfomatics to exemplify how biological questions can benefit from a machine learning approach. Finally, we discuss how cooperation between biologists and machine learners might be made smoother.