{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/62757"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/62757","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Multicoil-HMM : improved prediction of coiled-coil oligomer state from sequence","abstract":"The Multicoil-HMM algorithm offers improved prediction of coiled-coil oligomerization state. The algorithm combines the pairwise correlations of the Multicoil method with the flexibility of HMM methods. The resulting method incorporates predictors deemed important by a multinomial logistic regression to distinguish between the dimer, trimer and non-coiled coil oligomerization states. The Multicoil-HMM algorithm shows significantly improved oligomer state prediction over a retrained Multicoil algorithm, which is currently the state-of-the-art. The general strategy of using multinomial regression on predictors that can be simulated by HMMs while abandoning the probabilistic interpretation of HMMs may be useful in other machine learning applications.","abstract_html":"The Multicoil-HMM algorithm offers improved prediction of coiled-coil oligomerization state. The algorithm combines the pairwise correlations of the Multicoil method with the flexibility of HMM methods. The resulting method incorporates predictors deemed important by a multinomial logistic regression to distinguish between the dimer, trimer and non-coiled coil oligomerization states. The Multicoil-HMM algorithm shows significantly improved oligomer state prediction over a retrained Multicoil algorithm, which is currently the state-of-the-art. The general strategy of using multinomial regression on predictors that can be simulated by HMMs while abandoning the probabilistic interpretation of HMMs may be useful in other machine learning applications.","abstract_has_math":false,"creators":["Trigg, Jason (Jason A.)"],"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":["Bonnie Berger."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:20:59Z","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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