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Analysis of an optimal hidden Markov model for secondary structure prediction
2006
BMC Structural Biology
Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method
doi:10.1186/1472-6807-6-25
pmid:17166267
pmcid:PMC1769381
fatcat:nanhwhek6zebbobxl5woliswgq