Dataset and Code for "Predicting CircRNA-disease Associations through Linear Neighborhood Label Propagation Method"
Dataset1/association.csv
is the circRNA-disease association matrix ofDataset1
, which contains 331 associations between 312 circRNAs and 40 diseases.Dataset1/all_circRNAs.csv
contains all the circRNAs, corresponding to the rows of the association matrix.Dataset1/all_diseases.csv
contains all the diseases, corresponding to the columns of the association matrix.
Dataset2/association.csv
is the circRNA-disease association matrix ofDataset2
, which contains 650 associations between 603 circRNAs and 88 diseases.Dataset2/all_circRNAs.csv
contains all the circRNAs, corresponding to the rows of the association matrix.Dataset2/all_diseases.csv
contains all the diseases, corresponding to the columns of the association matrix.
-
case_study.py
calculates score matrices of case studies onDataset1
andDataset2
respectively. -
LNLP_method.py
contains our method function, that islinear_neighbor_predict
. -
LNLP_evaluation.py
implements LOOCV of CD-LNLP onDataset1
.
-
case_study_scores
Dataset1_scores.csv
is the score matrix of case study onDataset1
.Dataset2_scores.csv
is the score matrix of case study onDataset2
.
-
Dataset1_result/disease
For every disease in
Dataset1
, the candidate circRNAs are in the text file named as the disease's name inDataset1_result/disease
folder in descending order of score. -
Dataset2_result/disease
For every disease in
Dataset2
, the candidate circRNAs are in the text file named as the disease's name inDataset2_result/disease
folder in descending order of score. -
evaluation_result/loocv
evaluation_result/loocv
contains our method's evaluation result on LOOCV.0.1_0.9_1.0_loo.csv
contains the values of 6 metrics.0.1_0.9_1.0_loo_pr_x.csv
contains the values of recall on different thresholds.0.1_0.9_1.0_loo_pr_y.csv
contains the values of precision on different thresholds.0.1_0.9_1.0_loo_roc_x.csv
contains the values of False Positive Rate on different thresholds.0.1_0.9_1.0_loo_roc_y.csv
contains the values of True Positive Rate on different thresholds.