Name
Title | ||
|---|---|---|
SPOT-1D2: Improving Protein Secondary Structure Prediction using High Sequence Identity Training Set and an Ensemble of Recurrent and Residual-convolutional Neural Networks |
Abstract | ||
|---|---|---|
Protein secondary structure prediction has been a long-standing problem in computational biology. Recent advances in deep contextual learning have enabled its performance in three-state prediction closer to the theoretical limit at 88–90%. Here, we showed that a large training set with 95% sequence identity cutoff can improve prediction of secondary structures even for those unrelated test sequenc... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CIBCB49929.2021.9562849 | 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Keywords | DocType | ISBN |
Training,Proteins,Three-dimensional displays,Computational modeling,Neural networks,Training data,Computer architecture | Conference | 978-1-6654-0112-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaspreet Singh | 1 | 0 | 2.70 |
| Jaspal Singh Saini | 2 | 5 | 4.58 |
| Kuldip K. Paliwal | 3 | 1890 | 154.90 |
| Andrew Busch | 4 | 0 | 0.34 |
| Yaoqi Zhou | 5 | 109 | 8.72 |