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Biologically inspired intelligent decision making: A commentary on the use of artificial neural networks in bioinformatics

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...

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Detalles Bibliográficos
Autores principales: Manning, Timmy, Sleator, Roy D, Walsh, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Landes Bioscience 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049912/
https://www.ncbi.nlm.nih.gov/pubmed/24335433
http://dx.doi.org/10.4161/bioe.26997
Descripción
Sumario: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.