Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines

Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines

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 t...

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Bibliographic Details
Main Authors: Gassend, B., O'Donnell, C. W., Thies, W., Lee, A., van Dijk, M., Devadas, S.
Other Authors: Computation Structures
Language:en_US
Published: 2005
Online Access:http://hdl.handle.net/1721.1/30571
Description
Summary: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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