| At line 1 changed 2 lines |
| __ncRNAP__: De novo prediction of non-coding RNA-protein interactions |
| * Last updated: July 21, 2012 |
| __ncRNAP__: De novo prediction of RNA-protein interactions from sequence information |
| * Last updated: Sept. 14, 2012 |
| At line 8 changed one line |
| In this paper, we combined bioinformatics and experimental approach to de novo inferring non-coding RNA-protein interactions, and further conducted biological experiments to validate its effectiveness. We predicted protein-RNA interactions by an extended naïve-Bayes-classifier. Our predictor achieved high prediction accuracy. In particular, we demonstrate that the proposed method performs superior to the other machine-learning-based models for predicting protein-RNA interaction. We also conducted ncRNA pull-down experiments and identified interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrate the effectiveness of our proposed method. |
| In this paper, we combined bioinformatics and experimental approach to de novo inferring RNA-protein interactions, especially ncRNA-protein, and further conducted biological experiments to validate its effectiveness. We predicted protein-RNA interactions by an extended naïve-Bayes-classifier. Our predictor achieved high prediction accuracy. In particular, we demonstrate that the proposed method performs superior to the other machine-learning-based models for predicting protein-RNA interaction. We also conducted ncRNA pull-down experiments and identified interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrate the effectiveness of our proposed method. |
| At line 10 changed one line |
| Our method is particular suitable for studying function roles for long non-coding RNA (lncRNAs), a novel class of RNA molecules, that exhibit rising interest within the research community in recent years. . You can find the supporting materials or other resources which referred in the paper. Any questions, please contact Zhi-Ping Liu by zpliu AT sibs.ac.cn. Pay attention to the users: You can use and redistribute the data and code if you accept GNU General Public License (GPL). |
| Our method is particular suitable for studying function roles for long non-coding RNA (lncRNAs), a novel class of RNA molecules, that exhibit rising interest within the research community in recent years. You can find the supporting materials or other resources which referred in the paper. Pay attention to the users: You can use and redistribute the data and code if you accept GNU General Public License (GPL). |
| At line 12 added 2 lines |
| Any questions, please contact Dr. Zhi-Ping Liu by zpliu@sibs.ac.cn. |
|
| At line 13 changed one line |
| * Ying Wang, Xiaowei Chen, Zhi-Ping Liu, Qiang Huang, Yong Wang, Xiang-Sun Zhang, Runsheng Chen, and Luonan Chen: De novo prediction of non-coding RNA-protein interactions. 2012, in submission. |
| * Ying Wang, Xiaowei Chen, Zhi-Ping Liu, Qiang Huang, Yong Wang, Xiang-Sun Zhang, Runsheng Chen, and Luonan Chen: De novo prediction of RNA-protein interactions from sequence information. 9(1):133-142, 2013. |
