A hierarchical deep learning model for predicting ncRNA-protein interaction.
The sample, data and result directories contain model codes, tested data sets and generated results, respectively. The depended python packages are listed in requirements.txt. The package versions should be followed by users in their environments to achieve the supposed performance.
The program is in Python 3.6 using Keras and Tensorflow backends. Use the below bash command to run RPITER.
python rpiter.py -d datasetThe parameter of dataset could be RPI369, RPI488, RPI1807, RPI2241 or NPInter. Then, RPITER will perform 5-fold cross validation on the specific dataset.
The widely used RPI benchmark datasets are organized in the data directory.
| Dataset | #Positive pairs | #Negative pairs | RNAs | Proteins | Reference |
|---|---|---|---|---|---|
| RPI369 | 369 | 0 | 332 | 338 | [1] |
| RPI488 | 243 | 245 | 25 | 247 | [2] |
| RPI1807 | 1807 | 1436 | 1078 | 3131 | [3] |
| RPI2241 | 2241 | 0 | 841 | 2042 | [1] |
| NPInter | 10412 | 0 | 4636 | 449 | [4] |
For any questions, feel free to contact me by chengpengeace@gmail.com or start an issue instead.
[1] Muppirala, U.K.; Honavar, V.G.; Dobbs, D. Predicting RNA-Protein Interactions Using Only Sequence Information. Bmc Bioinformatics 2011, 12. doi:Artn 489 10.1186/1471-2105-12-489.
[2] Pan, X.Y.; Fan, Y.X.; Yan, J.C.; Shen, H.B. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. Bmc Genomics 2016, 17. doi:ARTN 582 10.1186/s12864-016-2931-8.
[3] Suresh, V.; Liu, L.; Adjeroh, D.; Zhou, X.B. RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Research 2015, 43, 1370–1379. doi:10.1093/nar/gkv020.
[4] Yuan, J.;Wu,W.; Xie, C.Y.; Zhao, G.G.; Zhao, Y.; Chen, R.S. NPInter v2.0: an updated database of ncRNA interactions. Nucleic Acids Research 2014, 42, D104–D108. doi:10.1093/nar/gkt1057.