miRNA target prediction with PU Learning and One-Class Classification

This code uses machine learning in the form of two PU Learning techniques (Two-Step and PU Bagging) and two One-Class Classification techniques (One-Class SVM and Isolation Forest) for predicting functional and non-functional targets in the problem of miRNA target Prediction. Two supervised methods (Random Forest and SVM) are also used for comparison to the results obtained with PU Learning and OCC.

Python libraries needed

• python=3.7
• pandas
• numpy
• matplotlib
• scikit-learn
• imbalanced-learn

Dataset

The dataset used can be found at https://drive.google.com/open?id=1SPVYiqNMeOiwFasTUHtiFDx81ji_xCYb

Only the .att file is needed and it should be added to a "datasets" folder inside the main folder.

Files Generated

All files generated on each run will be stored inside a "executions" folder.

Startup File

The main script file that should be run is the "tarbasePU.py" file.