Improved splice site detection in Genie
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Markov Encoding for Detecting Signals in Genomic Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Open source clustering software
Bioinformatics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Bioinformatics
A hidden Markov modelwith binned duration algorithm
IEEE Transactions on Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
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We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate aHMMstructure identification platformthat is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.