Title :
On generalization of multilayer neural network applied to predicting protein secondary structure
Author :
Nakayama, Kenji ; Hirano, Akihiro ; Fukumura, Ken-Ichi
Author_Institution :
Dept. of Inf. & Syst. Eng., Kanazawa Univ., Japan
Abstract :
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.
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; prediction theory; proteins; random noise; amino acids; generalization; learning process; multilayer neural network; multimodal neural network; prediction accuracy; protein secondary structure; random noise; single neural network; Accuracy; Amino acids; Bioinformatics; Encoding; Genomics; Humans; Multi-layer neural network; Neural networks; Optimization methods; Proteins;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380114