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 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).
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Gassend, B., O’Donnell, C.W., Thies, W., Lee, A., van Dijk, M., Devadas, S. (2006). Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds) Pattern Recognition in Bioinformatics. PRIB 2006. Lecture Notes in Computer Science(), vol 4146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11818564_11
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