Multicoil-HMM : improved prediction of coiled-coil oligomer state from sequence

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Trigg, Jason (Jason A.)

Advisor dc:contributor.advisor

• Bonnie Berger.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/62757

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/62757