Neural Networks

Cathy H Wu, Computer and Information Sciences, Newark, Delaware, USA
Jerry W McLarty, Louisiana State University Health Sciences Center, Shreveport, Louisiana, USA
Li Liao, Computer and Information Sciences, Newark, Delaware, USA

Published online: September 2010

DOI: 10.1002/9780470015902.a0005268.pub2

Artificial neural networks are a form of machine learning, inspired by biological neural networks, which can be trained by examples to recognise patterns, classify data and make predictions. They have been used in a wide variety of problems in the fields of life sciences, such as predicting protein secondary structures, classifying protein families and identifying single nucleotide polymorphism. Problems in life sciences in general and in bioinformatics in particular present challenges to the design and training of neural networks and to the interpretation of networks outputs as well, including complex structured data and limited amount of training examples. In addressing these issues and limitations, significant progress has been made, including ways to design the neural networks to incorporate prior knowledge, to make automatic formulation of network architecture for optimal performance and to construct hybrid systems to leverage advantages of various learning methods.

Key Concepts:

  • Artificial neural networks are a means of mapping input data to output data in such a manner as to perform complex functions such as pattern recognition, classification, prediction and knowledge extraction, that are increasingly useful in bioinformatics applications.
  • There are numerous kinds of neural networks, classified by their architecture (number of layers, number of processing units, interconnections, etc.) and how they are trained to perform.
  • Neural networks have been successfully applied to numerous areas of genomic and proteomic research; the number of such applications has grown explosively in the past few years.
  • One of the most difficult challenges in constructing and training neural networks is often in determining the content and features of data to be used for training and ultimately using the neural networks, that is feature extraction, preprocessing.
  • Hybrid systems, which combine neural networks with other artificial intelligence and statistical techniques, are a promising area of research.

Keywords: artificial intelligence; classification; encoding; sequence analysis; gene recognition

Figure 1. A simple, multilayer neural network. The units marked f perform weighted sums and threshold functions. Input units simply pass the input data to the hidden layer. Desired output results (target values) are supplied during training for comparison with the actual network output. Weights, partially shown as W11, W12, etc., are adjusted to minimise the error function. Once trained, a network no longer requires the target values and may be used to process new information.
Figure 2. Schema of application of neural networks to genome informatics: important features are extracted from DNA or protein sequences, encoded into a vector of real numbers, and then applied to a neural network for training or prediction. Output results may also be manipulated and finally results for given applications evaluated or utilised.
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Related Sub-Topics:

Bioinformatics | Evolutionary Genomics | Molecular Evolution | Computational Molecular Biology
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Article Title: Neural Networks
 
How to Cite close
Wu, Cathy H, McLarty, Jerry W, and Liao, Li(Sep 2010) Neural Networks. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0005268.pub2]