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Ieee transactions on computational biology and bioinformatics, tcbb-2014-03-0071 1 a deep learning network approach to ab initio protein secondary structure prediction.
| Content Provider | CiteSeerX |
|---|---|
| Author | Spencer, Matt Eickholt, Jesse Cheng, Jianlin |
| Abstract | Abstract — Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 % and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7 % and a Sov accuracy of 74.2%. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Deep Learning Network Approach Initio Protein Secondary Structure Prediction Ieee Transaction Computational Biology Bioinformatics Neural Network S Prediction Prediction Cannot Sov Accuracy Deep Learning Network Architecture Deep Learning Position-specific Scoring Matrix Cuda Software Rapid Discovery Deep Network Recent Development Independent Test Data Set Novel Deep Learning Technique Previous Method Refined Prediction Optimal Parameter Many Wonder S Predictor Tertiary Structure Prediction Training Process Trained Deep Network Q3 Accuracy Important Role Deep Network Architecture |
| Content Type | Text |
