Computational Genomics, Machine Learning

SVMs in computational Biology

Support vector machines (SVMs) are a set of supervised learning methods, they could used in statistical patterns classification and recognize.  As its easy extension and  could be deal the high dimension data, its could be used widely in computational biology. Here are two paper published in Education issue of  Plos Computational, which introduced Machine Learning and SVMs to Computational Biology:

1. Support Vector Machines and Kernels for Computational Biology

Asa Ben-Hur1#, Cheng Soon Ong2,3#¤, Sören Sonnenburg4, Bernhard Schölkopf3, Gunnar Rätsch2*

1 Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America, 2 Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany, 3 Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 4 Fraunhofer Institute FIRST, Berlin, Germany

Introduction

The increasing wealth of biological data coming from a large variety of platforms and the continued development of new high-throughput methods for probing biological systems require increasingly more sophisticated computational approaches. Putting all these data in simple-to-use databases is a first step; but realizing the full potential of the data requires algorithms that automatically extract regularities from the data, which can then lead to biological insight.

2. Machine Learning and Its Applications to Biology

Adi L. Tarca, Vincent J. Carey, Xue-wen Chen, Roberto Romero, Sorin Drăghici*

Introduction

The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. That is, the product of machine learning is a classifier that can be feasibly used on available hardware. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input. This second facet is inevitably vague, but the basic objective is that the use of automatic algorithm construction methods can minimize the possibility that human biases could affect the selection and performance of the algorithm. Both the creation of the algorithm and its operation to classify objects or predict events are to be based on concrete, observable data.


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