Biologically inspired intelligent decision making

Abstract

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

Keywords: :

• artificial neural networks
• bioinformatics
• gene identification
• gene-gene interaction
• genome wide association study
• multilayer perceptron
• protein structure prediction

10.4161/bioe.26997

Disclosure of Potential conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

This work was funded by the FP7-PEOPLE-2012-IAPP grant ClouDx-i to R.D.S. and P.W., and a Cork Institute of Technology Rísam Scholarship to T.M.