##GcForest-PPI
Prediction of protein-protein interactions based on elastic net and deep forest.
###GcForest-PPI uses the following dependencies:
- python 3.6
- numpy
- scipy
- scikit-learn
- gcForst gcForest is developed by Zhou et al.[1,2], which can be download from http://lamda.nju.edu.cn/code_gcForest.ashx.
[1] Z.H. Zhou, J. Feng, Deep forest: towards an alternative to deep neural networks, In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, pp. 3553-3559. [2] Z.H. Zhou, J. Feng, Deep forest, National Science Review 6 (2019) 74-86.
###Guiding principles:
**The dataset file contains the S. cerevisiae, H. pylori, the independent dataset and network dataset.
**Feature extraction
- Evolutionary information: obtain_pssm.py is the implementation of AAC-PSSM and DPC-PSSM.
- Physicochemical_information: PAAC_Yeast.m is the implementation of PAAC. Auto_yeast.m is the implementation of Auto.
- Sequence_information: yeast_CTDC.py, yeast_CTDT.py, yeast_CTDD.py are the implementation of CTD ExMI.m is the implementation of MMI.
** Dimensional reduction: yeast_elastic_end.py represents the elastic. yeast_KPCA.py represents KPCA. yeast_LLE.py represents LLE. yeast_PCA.py represents PCA. yeast_SE.py represents SE. yeast_SSDR.py represents SSDR. yeast_TSVD.py represents SVD.
** Classifier: yeast_gcforest.py is the implementation of GcForest. yeast_Ad.py is the implementation of AdaBoost. yeast_KNN.py is the implementation of KNN. yeast_LR.py is the implementation of LR. yeast_NB.py is the implementation of NB. yeast_RF.py is the implementation of RF. yeast_SVM.py is the implementation of SVM.
** independent_test: The independent_test file contains the code of the test of independent dataset and network dataset.