Name
Abstract | ||
|---|---|---|
We developed a multi-modal feed-forward neural network to predict the secondary structure of proteins. Several neural networks are used together and the final prediction results are decided by majority rule. We used 6137 residues to train and test the method. The average accuracy of the prediction is 66%, which is about 6.9% higher than single neural network |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/IJCNN.2002.1005483 | Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference |
Keywords | Field | DocType |
biology computing,feedforward neural nets,multilayer perceptrons,proteins,majority rule,multimodal feedforward neural network,neural network training,protein secondary structure,structure prediction,testing,secondary structure,amino acids,databases,neural networks,encoding,neural network,protein engineering,feed forward neural network,feedforward neural networks | Data mining,Computer science,Time delay neural network,Artificial intelligence,Artificial neural network,Majority rule,Feedforward neural network,Pattern recognition,Probabilistic neural network,Protein secondary structure,Modal,Machine learning,Encoding (memory) | Conference |
Volume | ISSN | Citations |
1 | 1098-7576 | 5 |
PageRank | References | Authors |
0.60 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhu, H. | 1 | 5 | 0.60 |
| Yoshihara, I. | 2 | 5 | 0.60 |
| Kunihito Yamamori | 3 | 15 | 8.78 |
| Moritoshi Yasunaga | 4 | 178 | 33.03 |