##MpsLDA-ProSVM
predicting multi-label protein subcellular localization by wMLDAe dimensionality reduction and ProSVM classifier
###Guiding principles:
**The dataset file contains Gram-negative bacteria dataset, Gram-positive bacteria dataset , plant dataset and virus dataset.
**Feature extraction
- Evolutionary information: psepssm.m is the implementation of PsePSSM.
- Physicochemical_information: PAAC.m,mainpseaac.m is the implementation of PseAAC.
- Sequence_information: CTriad.py is the implementation of CT.
- Annotation information： Gene Ontology can be found from http://www.ebi.ac.uk/GOA/. ** Dimensional reduction: DMLDA_transform.m represents the DMLDA. MDDM_transform.m represents MDDM. PCA_transform.m represents PCA. MLSI_transform represents MLSI. MVMD_transform represents MVMD.
** Classifier: 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. ML_RBF_train.m, ML_RBF_test.m is the implementation of ML_RBF. RankSVM_train.m,RankSVM_test.m is the implementation of RankSVM. orderSynthetic.m, ProSVM.m is the implementation of ProSVM.
** independent_test:
The independent_test file contains the code of the test of independent dataset.
And you can run the demo.m in MATLAB.