Multicoil-HMM : improved prediction of coiled-coil oligomer state from sequence
Name
717726195-MIT.pdf
Description
Full printable version
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
a280f4a9ae8b6513be3da710300227a7
Author(s)
Trigg, Jason (Jason A.)
Advisor(s)
Bonnie Berger.
Alternative Title
Multicoil-Hidden Markov Model
Date Issued
2010
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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