title: Artificial intelligence techniques for bioinformatics.
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.
authors.affiliation: School of Engineering and Computer Sciences, University of Exeter, UK. A.Narayanan@ex.ac.uk
authors.forename: Ajit
authors.initials: A
authors.lastname: Narayanan
authors.forename: Edward C
authors.initials: EC
authors.lastname: Keedwell
authors.forename: Björn
authors.initials: B
authors.lastname: Olsson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D016333:HIV Protease / Q000378:metabolism; D006801:Humans; D007938:Leukemia / Q000235:genetics; D008954:Models, Biological; D008958:Models, Molecular; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D017433:Protein Structure, Secondary; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data; D013379:Substrate Specificity
chemical_list: D016333:HIV Protease