Name
Abstract | ||
|---|---|---|
Determining the flexibility of structured biomolecules is important for understanding their biological functions. One quantitative measurement of flexibility is the atomic Debye-Waller factor or temperature B-factor. Most existing studies are limited to temperature B-factors of proteins and their prediction. Only one method attempted to predict temperature B-factors of ribosomal RNA. Here, we developed and compared machine-learning techniques in prediction of temperature B-factors of RNAs. The best model based on Support Vector Machines yields Pearson's correction coefficient at 0.51 for fivefold cross validation and 0.50 for the independent test. Analysis of the performance indicates that the model has the best performance on rRNAs, tRNAs, and protein-bound RNAs, for long chains in particular. The server is available at . (c) 2017 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1002/jcc.25124 | JOURNAL OF COMPUTATIONAL CHEMISTRY |
Keywords | Field | DocType |
RNA flexibility, temperature B-factor, support vectors regression | Biomolecule,RNA,Mathematical optimization,Biological system,Ribosomal RNA,Support vector machine,Chemistry,B factor,Cross-validation | Journal |
Volume | Issue | ISSN |
39 | 8 | 0192-8651 |
Citations | PageRank | References |
2 | 0.36 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivantha Guruge | 1 | 2 | 0.36 |
| Ghazaleh Taherzadeh | 2 | 20 | 3.74 |
| Jian Zhan | 3 | 4 | 1.08 |
| Yaoqi Zhou | 4 | 109 | 8.72 |
| Yuedong Yang | 5 | 196 | 23.47 |