An ensemble deep learning with multi-scale feature combination for ncRNA-protein interaction prediction.
The untils, 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 EDLMFC.
python main.py -d datasetThe parameter of dataset could be RPI488, RPI1807 and NPInter v2.0. Then, EDLMFC will perform 5-fold cross validation on the specific dataset.
The widely used RPI benchmark datasets are organized in the data directory.
Due to the limitation of the hardware conditions of the selected RNA secondary structure method, it can only predict the secondary structure of RNA with a length of no more than 500 nucleotides, so we preprocessed the data.
Dataset | #Positive pairs | #Negative pairs | RNAs | Proteins |Reference
Original set
RPI1807 1807 1436 1078 3131 [1]
NPInter v2.0 10412 10412 4636 449 [2]
RPI488 243 245 25 247 [1]
Optimal set
RPI1807 652 221 646 868 [1]
NPInter v2.0 1943 1943 513 448 [2]
RPI488 43 233 13 155 [1]
For any questions, feel free to contact me by WangJingJing@emails.bjut.edu.cn or start an issue instead.
[1] 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.
[2] 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.