Abstract
Prediction of protein secondary structure is considered to be an important step toward elucidating the three-dimensional structure and function of proteins. We have developed a multimodal neural network (MNN) to predict protein secondary structure. The MNN is composed of several subclassifiers for single-state predictions using neural networks and a decision neural network (DNN). Each subclassifier employs a number of subnetworks to predict the single-state of the secondary structure individually and produces the final results by majority decision. The DNN uses a three-layer neural network to produce the final overall prediction from the outputs of the single-state predictions. The MNN gives an overall accuracy of 71.1% with corresponding Matthews correlation coefficients of CH = 0.62 and CE = 0.53. The prediction test is based on a database of 126 nonhomologous protein sequences.
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This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.
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Zhu, H., Yoshihara, I., Yamamori, K. et al. A multimodal neural network with single-state predictions for protein secondary structure. Artif Life Robotics 8, 168–173 (2004). https://doi.org/10.1007/s10015-004-0306-8
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DOI: https://doi.org/10.1007/s10015-004-0306-8