We can directly download by clicking the link.

Note: The package will download in zip format (.zip) named iPro70-master.zip.

or,

Cloning a repository syncs it to our local machine. After clone, we can add and edit files and then push and pull updates.

• Clone over HTTPS: user@machine:~$ git clone https://github.com/mrzResearchArena/iPro70.git
• Clone over SSH: user@machine:~$ git clone [email protected]:mrzResearchArena/iPro70.git

Note: If the clone was successful, a new sub-directory appears on our local drive. This directory has the same name (iPro70) as the GitHub repository that we cloned.

Major (Generate Features):

• Install: python (version >= 3.5)
• Install: numpy (version >= 1.13.0)

Minor (Performance Measures):

• Install: sklearn (version >= 0.19.0)
• Install: pandas (version >= 0.21.0)
• Install: matplotlib (version >= 2.1.0)

Using PIP: pip install <package name>

user@machine:~$ pip install scikit-learn

or,

Using anaconda environment: conda install <package name>

user@machine:~$ conda install scikit-learn

Run command on your console or terminal.

user@machine:~$ python main.py --fullDataset=1 --optimumDataset=1 --fasta=/home/user/iPro70/DNADatasets/FASTA.txt --label=/home/user/iPro70/DNADatasets/Label.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -full=1 -optimum=1 -fa=/home/user/iPro70/DNADatasets/FASTA.txt -la=/home/user/iPro70/DNADatasets/Label.txt -ktuple=3 -kgap=5 -pseudo=1 -f11=1 -f12=1 -f13=1 -f21=1 -f22=1 -f23=1 -f31=1 -f32=1

user@machine:~$ python main.py --testDataset=1 --fasta=/home/user/iPro70/DNADatasets/demoFASTA.txt --label=/home/user/iPro70/DNADatasets/demoLabel.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -test=1 -fa=/home/user/iPro70/DNADatasets/demoFASTA.txt -la=/home/user/DNADatasets/demoLabel.txt -ktuple=3 -kgap=5 -pseudo=1 -atgc=1 -f11=1 -f12=1 -f13=1 -f21=1 -f22=1 -f23=1 -f31=1 -f32=1

[ Comment: The = sign is optional. ]

Note #1: It will generate a full dataset named fullDataset.csv (if -full=1 or, --fullDataset==1)
Note #2: It will generate a selected features dataset named optimumDataset.csv (if -optimum=1 or, --optimumDataset==1), and It will also track the selected features index.
Note #3: It will generate a full dataset named testDataset.csv (if -test=1 or, --testDataset==1) [ Especially for the independent (testing) dataset purpose. ] [ 3.1.2. ]
Note #4: The process will run smoothly for valid FASTA sequences and row-wise class label.

Note: When sequence becomes DNA then X = { A, C, G, T }.

user@machine:~$ python runClassifiers.py --nFCV=10 --dataset=optimumDataset.csv --auROC=1 --boxPlot=1

Note #1: It will provide classification results (evaluationResults.txt) from the user provides binary class dataset (.csv format).
Note #2: Generate a ROC Curve (auROC.png).
Note #3: Generate an accuracy comparison via boxPlot (AccuracyBoxPlot.png).

user@machine:~$ python trainModel.py --dataset=optimumDataset.csv --model=LR

Note #1: It will provide a dumpModel.pkl from the user provides binary class dataset (.csv format).

Note: LR, SVM, KNN, DT, NB, Bagging, RF, AB, GB, and LDA represents Logistics Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree, Naive Bayes, Bagging, Random Forest, AdaBoost, Gradient Boosting, Linear Discriminant Analysis classifier respectively.

user@machine:~$ python evaluateModel.py --optimumDatasetPath=optimumDataset.csv --testDatasetPath=testDataset.csv

Note #1: Here, optimumDataset.csv, and testDataset.csv using as a traing dataset and test dataset respectively.

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[5] Bin Liu, Hao Wu, Deyuan Zhang, Xiaolong Wang, and Kuo-Chen Chou. Pse-analysis: a python package for dna/rna and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget, 8(8):13338, 2017.

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