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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines
verfasst von : Blaise Gassend, Charles W. O’Donnell, William Thies, Andrew Lee, Marten van Dijk, Srinivas Devadas
Erschienen in: Pattern Recognition in Bioinformatics
Verlag: Springer Berlin Heidelberg
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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
Q
α
value of 77.6% and a
SOV
α
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).