LCS Publication Details
Publication Title: Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines
Publication Author: Gassend, B.
Additional Authors: C. W. O'Donnell, W. Thies, A. Lee, M. van Dijk, S. Devadas
LCS Document Number: MIT-LCS-TR-1003
Publication Date: 10-6-2005
LCS Group: Computation Structures
Additional URL:
Our goal is to develop a state-of-the-art predictor with an intuitive and biophysically-motivated energy model through the use of Hidden Markov Support Vector Machines (HM-SVMs), a recent innovation in the field of machine learning. We focus on the prediction of alpha helices in proteins and show that using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Q_alpha value of 77.6% and a SOV_alpha value of 73.4%. We briefly describe how our method can be generalized to predicting beta strands and sheets.
To obtain this publication:

To purchase a printed copy of this publication please contact MIT Document Services.