Name
Title | ||
|---|---|---|
A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information. |
Abstract | ||
|---|---|---|
•SVM is explored to predict regulatory interactions in Arabidopsis. Experimentally validated regulatory relationships were collected as the positive samples.•Negative training samples were randomly selected TF–target pairs under some strategies.•Each gene pair was represented by incorporating the expression data and sequence information.•Through the jackknife test, our method reached an overall accuracy of 98.39% with the sensitivity of 94.88%, and the specificity of 93.82%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.compbiolchem.2014.04.003 | Computational Biology and Chemistry |
Keywords | Field | DocType |
Transcription factor,Expression profile,Sequence information,Support vector machines | Arabidopsis,Jackknife resampling,Biology,Support vector machine,Gene expression,Bioinformatics,Genetics,Transcription factor | Journal |
Volume | ISSN | Citations |
51 | 1476-9271 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqing Yu | 1 | 75 | 11.53 |
| Hongyun Gao | 2 | 14 | 1.15 |
| Xiaoqi Zheng | 3 | 59 | 6.32 |
| Chun Li | 4 | 11 | 1.35 |
| Jun Wang | 5 | 24 | 4.32 |