Abstract
A novel approach is developed for Protein Secondary Structure Prediction based on Bayesian Neural Networks (BNN). BNN usually outperforms the traditional Back-Propagation Neural Networks (BPNN) due to its excellent ability to control the complexity of the model. Results indicates that BNN has an average overall three-state accuracy Q 3 increase 3.65% and 4.01% on the 4-fold cross-validation data sets and TEST data set respectively, comparing with the traditional BPNN. Meanwhile, a so-calledcross-validation choice of starting values is presented, which will shorten the burn-in phase during the MCMC (Markov Chain Monte Carlo) simulation substantially.
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Shao, J., Xu, D., Wang, L., Wang, Y. (2005). Bayesian Neural Networks for Prediction of Protein Secondary Structure. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_65
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DOI: https://doi.org/10.1007/11527503_65
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