Hidden Markov Model for protein secondary structure
نویسندگان
چکیده
We address the problem of protein secondary structure prediction with Hidden Markov Models. A 21-state model is built using biological knowledge and statistical analysis of sequence motifs in regular secondary structures. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme. Prediction accuracy with single sequences reaches 65.3% and raises to 72% of correct classification with profile information.
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تاریخ انتشار 1999