Prediction of target genes and functional types of cis-regulatory modules in the human genome reveals their distinct properties

The training datasets for predicting funcitonal states of CRMs (file size too big, split into two smaller files) TrainData_1.zip TrainData_2.zip The code and models for training, evaluation, and testing: Train_evaluate_model.py LogisticRegression-CA.joblib The code for predicting active CRM in the whole genome: Predict-ENCODE.py Predicted active CRM in the heart-right-ventricle cells human-donorsENCDO967KID-heart-right-ventricle.CRM.minmax.zip human-donorsENCDO967KID-heart-right-ventricle.CRM.minmaxLogisticRegression.predict.zip Mann whitney U test: MWU-test.py Predictions of Regulations columns: CRM(chr-start-end) Gene(chr-start-end) q-value up regulaitons: Enhancer_Hic_Validated_Regulations_2000.uniq.HiC20.bed.zip down regulations: Silencer_Hic_Validated_Regulations_2000.uniq.HiC20.bed.zip