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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Language: | en_US |
| Published: |
2005
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| Online Access: | http://hdl.handle.net/1721.1/30571 |
| _version_ | 1811083447557423104 |
|---|---|
| author | Gassend, B. O'Donnell, C. W. Thies, W. Lee, A. van Dijk, M. Devadas, S. |
| author2 | Computation Structures |
| author_facet | Computation Structures Gassend, B. O'Donnell, C. W. Thies, W. Lee, A. van Dijk, M. Devadas, S. |
| author_sort | Gassend, B. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:33:13Z |
| id | mit-1721.1/30571 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:33:13Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/305712019-04-11T03:41:46Z Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines Gassend, B. O'Donnell, C. W. Thies, W. Lee, A. van Dijk, M. Devadas, S. Computation Structures 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. 2005-12-22T02:37:06Z 2005-12-22T02:37:06Z 2005-10-06 MIT-CSAIL-TR-2005-060 MIT-LCS-TR-1003 http://hdl.handle.net/1721.1/30571 en_US Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 15 p. 18110378 bytes 702915 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Gassend, B. O'Donnell, C. W. Thies, W. Lee, A. van Dijk, M. Devadas, S. Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title_full | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title_fullStr | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title_full_unstemmed | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title_short | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
| title_sort | secondary structure prediction of all helical proteins using hidden markov support vector machines |
| url | http://hdl.handle.net/1721.1/30571 |
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