Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo hóa học: " Research Article A Metastate HMM with Application to Gene Structure Identification in Eukaryotes"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Metastate HMM with Application to Gene Structure Identification in Eukaryotes | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 581373 18 pages doi 10.1155 2010 581373 Research Article A Metastate HMM with Application to Gene Structure Identification in Eukaryotes Stephen Winters-Hilt and Carl Baribault Computer Science Department University of New Orleans New Orleans LA 70148 USA Correspondence should be addressed to Stephen Winters-Hilt winters@cs.uno.edu Received 25 March 2010 Revised 18 August 2010 Accepted 16 November 2010 Academic Editor Ulisses Braga-Neto Copyright 2010 S. Winters-Hilt and C. Baribault. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We introduce a generalized-clique hidden Markov model HMM and apply it to gene finding in eukaryotes C. elegans . We demonstrate a HMM structure identification platform that 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 .