Name
Abstract | ||
|---|---|---|
Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, they usually have to sacrifice accuracy and may fail to fully leverage the potential of large datasets. Recently, it has been demonstrated that the motifs identified by DMDs can be significantly improved by maximizing the receiver-operating characteristic curve (AUC) met... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCBB.2017.2691325 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
DNA,Fasteners,Pulse width modulation,Refining,Scalability,Electronic mail,Support vector machines | Cutting-plane method,Hinge loss,Computer science,Support vector machine,Amino Acid Motifs,Algorithm,Regular polygon,Software,Artificial intelligence,Discriminative model,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
15 | 3 | 1545-5963 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Zhu | 1 | 218 | 5.97 |
| Hong-bo Zhang | 2 | 4 | 1.47 |
| De-Shuang Huang | 3 | 5532 | 357.50 |