{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/41646"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/41646","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Gene prediction with conditional random fields","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_html":"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_has_math":false,"creators":["Doherty, Matthew K"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["James Galagan and David DeCaprio."],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-22T22:21:46Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/41646"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.","Includes bibliographical references (p. 75-77)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Gene prediction with conditional random fields"]}]}],"canonical_facts":{"dc:contributor.advisor":["James Galagan and David DeCaprio."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Doherty, Matthew K"],"dc:date.accessioned":["2008-05-19T16:04:55Z"],"dc:date.available":["2008-05-19T16:04:55Z"],"dc:date.issued":["2007"],"dc:description":["Thesis (M. 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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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/41646"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Gene prediction with conditional random fields"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:46Z"}