| 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: |
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| Abstract: |
| 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. |
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