Mining biological data using self-organizing map.
This paper presents a novel method of mining biological data using a self-organizing map (SOM). After partitioning a set of protein sequences using SOM, conventional homology alignment is applied to each cluster to determine the conserved local motif (biological pattern) for the cluster. These local motifs are then regarded as rules for prediction and classification. In the application to the prediction of HIV protease cleavage sites in proteins, we found that the rules derived from this method are much more robust than those derived from the decision tree method.
Yang
Zheng Rong
ZR
Department of Computer Science, Exeter University, Exeter EX4 4PT, UK. Z.R.Yang@exeter.ac.uk
Chou
Kuo-Chen
KC
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology; D003628:Database Management Systems; D030541:Databases, Genetic; D003663:Decision Trees; D016333:HIV Protease / Q000737:chemistry; D006801:Humans; D009154:Mutation / Q000235:genetics; D009842:Oligopeptides / Q000737:chemistry / Q000235:genetics
D000596:Amino Acids; D009842:Oligopeptides; D016333:HIV Protease