title: A biological compression model and its applications.
abstract: A biological compression model, expert model, is presented which is superior to existing compression algorithms in both compression performance and speed. The model is able to compress whole eukaryotic genomes. Most importantly, the model provides a framework for knowledge discovery from biological data. It can be used for repeat element discovery, sequence alignment and phylogenetic analysis. We demonstrate that the model can handle statistically biased sequences and distantly related sequences where conventional knowledge discovery tools often fail.
authors: Clayton School of Information Technology, Monash University, Clayton, VIC, 3800, Australia. minhduc@monash.edu
authors: Minh Duc
authors: MD
authors: Cao
authors: Trevor I
authors: TI
authors: Dix
authors: Lloyd
authors: L
authors: Allison
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D044962:Data Compression / Q000706:statistics & numerical data*; D005103:Expert Systems; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D007257:Information Theory; D051188:Knowledge Bases; D008957:Models, Genetic; D015233:Models, Statistical; D010802:Phylogeny; D012091:Repetitive Sequences, Nucleic Acid; D016415:Sequence Alignment / Q000706:statistics & numerical data