Artificial intelligence techniques for bioinformatics.

Ajit Narayanan, Edward C Keedwell, Björn Olsson
Author Information
  1. Ajit Narayanan: School of Engineering and Computer Sciences, University of Exeter, UK. A.Narayanan@ex.ac.uk

Abstract

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.

MeSH Term

Algorithms
Artificial Intelligence
Biological Evolution
Cluster Analysis
Computational Biology
Computer Simulation
Gene Expression Profiling
HIV Protease
Humans
Leukemia
Models, Biological
Models, Molecular
Neural Networks, Computer
Oligonucleotide Array Sequence Analysis
Protein Structure, Secondary
Saccharomyces cerevisiae
Sequence Alignment
Substrate Specificity

Chemicals

HIV Protease

Word Cloud

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