2026-09-18T23:14:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416462022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
James Galagan and David DeCaprio.
Doherty, Matthew K
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T16:04:55Z
2008-05-19T16:04:55Z
2007
2007
http://hdl.handle.net/1721.1/41646
219708684
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 75-77).
The accurate annotation of an organism's protein-coding genes is crucial for subsequent genomic analysis. The rapid advance of sequencing technology has created a gap between genomic sequences and their annotations. Automated annotation methods are needed to bridge this gap, but existing solutions based on hidden Markov models cannot easily incorporate diverse evidence to make more accurate predictions. In this thesis, I built upon the semi-Markov conditional random field framework created by DeCaprio et al. to predict protein-coding genes in DNA sequences. Several novel extensions were designed and implemented, including a 29-state model with both semi-Markov and Markov states, an N-best Viterbi inference algorithm, several classes of discriminative feature functions that incorporate diverse evidence, and parallelization of the training and inference algorithms. The extensions were tested on the genomes of Phytophthora infestans, Culex pipiens, and Homo sapiens. The gene predictions were analyzed and the benefits of discriminative methods were explored.
by Matthew K. Doherty.
M.Eng.
77 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Gene prediction with conditional random fields
Applications of conditional random fields in bioinformatics
Thesis
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<Abstract>The accurate annotation of an organism's protein-coding genes is crucial for subsequent genomic analysis. The rapid advance of sequencing technology has created a gap between genomic sequences and their annotations. Automated annotation methods are needed to bridge this gap, but existing solutions based on hidden Markov models cannot easily incorporate diverse evidence to make more accurate predictions. In this thesis, I built upon the semi-Markov conditional random field framework created by DeCaprio et al. to predict protein-coding genes in DNA sequences. Several novel extensions were designed and implemented, including a 29-state model with both semi-Markov and Markov states, an N-best Viterbi inference algorithm, several classes of discriminative feature functions that incorporate diverse evidence, and parallelization of the training and inference algorithms. The extensions were tested on the genomes of Phytophthora infestans, Culex pipiens, and Homo sapiens. The gene predictions were analyzed and the benefits of discriminative methods were explored.</Abstract>
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