##Mps-mvRBRL
Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier
###Guiding principles:
**The dataset file contains Gram-positive bacteria dataset, plant dataset, virus dataset and Gram-negative bacteria dataset .
**Feature extraction
- psepssm.m is the implementation of PsePSSM.
- PSSM-TPC1.m,PSSM-TPC2.m,PSSM-TPC3.py,PSSM-TPC.py is the implementation of PSSM-TPC.
- PAAC.m,mainpseaac.m is the implementation of PseAAC.
- Dipeptide composition can be found from http://www.csbio.sjtu.edu.cn/bioinf/PseAAC/#.
- Gene Ontology can be found from http://www.ebi.ac.uk/GOA/.
** Differential Evolution:
- testFun.m,mutation.m,DE.m,crossover.m is the implementation of DE.
** Dimensional reduction:
- wMLDAb_transform.m, weight_Park2008_Binary.m represents the wMLDAb.
- MDDM_transform.m represents MDDM.
- PCA_transform.m represents PCA.
- MLSI_transform represents MLSI.
- MVMD_transform represents MVMD.
** Classifier:
- Predict.m, train_linear_RBRL_APG.m is the implementation of RBRL.
- LIFT.m is the implementation of LIFT.
- MLKNN_test.m,MLKNN_train.m are the implementation of MLKNN.
- ML_GKR.m is the implementation of ML_GKR.
- MIML_RBF_test.m, MIML_RBF_train.m is the implementation of ML_RBF.
- RankSVM_train.m,RankSVM_test.m is the implementation of RankSVM.
- MIML_kNN_test.m, MIML_kNN_train.m is the implementation of MIML_kNN.
** Demo:
- An example is included in the Demo file.
- And you can run the demo.m in MATLAB.