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 \feld 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\u000b value of 77:6% and a SOV\u000b 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).<\/p>\n","protected":false},"excerpt":{"rendered":"
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 \feld of machine learning. We focus on the prediction of alpha helices and show that by using HM-SVMs, a simple 7-state HMM with […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Workshop on Pattern Recognition in Bioinformatics (PRIB 2006). 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