Multicoil-HMM : improved prediction of coiled-coil oligomer state from sequence
Author(s)Trigg, Jason (Jason A.)
Multicoil-Hidden Markov Model
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
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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.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 26).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.