Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines
| dc.date.accessioned | 2005-12-22T02:37:06Z | |
| dc.date.accessioned | 2018-11-24T10:24:37Z | |
| dc.date.available | 2005-12-22T02:37:06Z | |
| dc.date.available | 2018-11-24T10:24:37Z | |
| dc.date.issued | 2005-10-06 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/30571 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/30571 | |
| dc.description.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. | |
| dc.format.extent | 15 p. | |
| dc.format.extent | 18110378 bytes | |
| dc.format.extent | 702915 bytes | |
| dc.language.iso | en_US | |
| dc.title | Secondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines |
Files in this item
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| MIT-CSAIL-TR-2005-060.pdf | 702.9Kb | application/pdf | View/ |
| MIT-CSAIL-TR-2005-060.ps | 18.11Mb | application/postscript | View/ |