Predicting secondary structure of all-helical proteins using hidden markov support vector machines

B. Gassend, C.W. O'Donnell, W. Thies, A. Lee, M. Van Dijk, S. Devadas

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Our goal is to develop a state-of-the-art secondary structure 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 and show that by using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Qα value of 77.6% and a SOVα value of 73.4%. As detailed in an accompanying technical report [11], these performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). © Springer-Verlag Berlin Heidelberg 2006.
Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics - International Workshop, PRIB 2006, Proceedings
PublisherSpringer Verlag
Pages93-104
ISBN (Print)3540374469, 9783540374466
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventInternational Workshop on Pattern Recognition in Bioinformatics, PRIB 2006 - , China
Duration: 20 Aug 200620 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Pattern Recognition in Bioinformatics, PRIB 2006
Country/TerritoryChina
Period20/08/0620/08/06

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