Name
Abstract | ||
|---|---|---|
Protein structure prediction has always been an important issue in bioinformatics field. This paper proposes an HP model optimization method based on reinforcement learning, which is a new attempt in the area of protein structure prediction. It does not require external supervision as the agent can find the optimal solution from the reward function in the training process. And the method also decreases computational complexity through making the time complexity of the algorithm has a linear relationship with the length of protein sequence. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-95933-7_46 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II |
Keywords | Field | DocType |
Reinforcement learning, HP model, Structure prediction | Protein structure prediction,Protein sequencing,Computer science,Artificial intelligence,Time complexity,Machine learning,Computational complexity theory,Reinforcement learning | Conference |
Volume | ISSN | Citations |
10955 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ru Yang | 1 | 0 | 0.68 |
| Hongjie Wu | 2 | 4 | 5.90 |
| Qiming Fu | 3 | 3 | 5.84 |
| Tao Ding | 4 | 15 | 8.48 |
| Cheng Chen | 5 | 97 | 32.69 |