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Application of Maximum Entropy Markov Models on the Protein Secondary Structure Predictions

By Yohan Kim

Abstract

An application of a new probabilistic modeling framework, Maximum Entropy Markov Model, on the protein secondary structure prediction problem is described [6]. As in previous domains of problem, such as the task of segmenting a body of text, within which MEMM was applied [6], the secondary structure prediction problem requires labeling an observation sequence of alphabets. This paper is an exploratory effort that attempts to establish the feasibility of using the new framework on this longstanding problem. Our initial results produced with a rather simple MEMM model show promise ( 58% accuracy) and suggest directions for further improvements

Year: 2001
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.8718
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