2026-03-14T01:58:13Z https://kanazawa-u.repo.nii.ac.jp/oai

oai:kanazawa-u.repo.nii.ac.jp:00007746 2024-06-20T06:14:55Z 934:935:936

On generalization of multilayer neural network applied to predicting protein secondary structure Nakayama, Kenji 353 00207945 00207945 Hirano, Akihiro 377 70303261 70303261 Fukumura, K. 10499 A learning process of a single neural network (SNN) to improve prediction accuracy of protein secondary structure is optimized. The protein secondary structures are predicted using a multiple alignment of amino acid as the input data. A multi-modal neural network (MNN) has been proposed to improve the precision of prediction. This method uses five independent neural networks, and the final decision is made by averaging all outputs of five SNNs. In the proposed method, the same prediction accuracy can be achieved by using only a single NN and optimizing a learning process. In a learning process of protein structure prediction, over learning is easily occurred. So, the learning process is optimized so as to avoid the over learning. For this purpose, small learning rates, adding small random noise to the input data, and updating the connection weights by the average in some group are useful. The prediction accuracy 58% obtained by using the conventional SNN is improved to 66%, which is the same accuracy of the MNN, which needs five SNNs. conference paper IEEE(Institute of Electrical and Electronics Engineers) 2004-07-01 VoR application/pdf IEEE International Conference on Neural Networks - Conference Proceedings 2 1209 1213 1098-7576 https://kanazawa-u.repo.nii.ac.jp/record/7746/files/TE-PR-NAKAYAMA-K-1209.pdf http://hdl.handle.net/2297/6794 https://kanazawa-u.repo.nii.ac.jp/records/7746 eng