Difference between revisions of "BIO Machine learning"

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==Further reading and resources==
 
==Further reading and resources==
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|abstract= The field of machine learning provides useful means and tools for finding accurate solutions to complex and challenging biological problems. In recent years a class of learning algorithms namely kernel methods has been successfully applied to various tasks in computational biology. In this article we present an overview of kernel methods and support vector machines and focus on their applications to biological sequences. We also describe a new class of approaches that is termed as deep learning. These techniques have desirable characteristics and their use can be highly effective within the field of computational biology.
 
|abstract= The field of machine learning provides useful means and tools for finding accurate solutions to complex and challenging biological problems. In recent years a class of learning algorithms namely kernel methods has been successfully applied to various tasks in computational biology. In this article we present an overview of kernel methods and support vector machines and focus on their applications to biological sequences. We also describe a new class of approaches that is termed as deep learning. These techniques have desirable characteristics and their use can be highly effective within the field of computational biology.
 
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Revision as of 22:16, 14 January 2014

Machine Learning


This page is a placeholder, or under current development; it is here principally to establish the logical framework of the site. The material on this page is correct, but incomplete.


Overview of "classical" and current approaches to machine learning.



 

Introductory reading

Machine learning


 

Introduction

Paradigms

Neural Networks

Neural network

Deep learning

Hidden Markov Models

Hidden Markov model


Support Vector Machines

Support vector machine


Bayesian Networks

Bayesian network

Training sets

Gold standards as true positives and the problem of generating true negatives from non-observed data...


ROC and associated metrics

Receiver operating characteristic


Machine learning in R

Joshua Reich has posted a self-contained R-script for a number of machine learning topics. See below.


 

 

Further reading and resources

Hassanien et al. (2013) Computational intelligence techniques in bioinformatics. Comput Biol Chem 47:37-47. (pmid: 23891719)

PubMed ] [ DOI ]

Salakhutdinov & Hinton (2012) An efficient learning procedure for deep Boltzmann machines. Neural Comput 24:1967-2006. (pmid: 22509963)

PubMed ] [ DOI ]

Hinton et al. (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527-54. (pmid: 16764513)

PubMed ] [ DOI ]

Lodhi, H. (2012) Computational biology perspective: kernel methods and deep learning. WIREs: Computational Statistics 4(5):455-465.
(pmid: None)Source URL ][ DOI ]