Accept journal IEEE Journal of Biomedical and Health Informatics ## Paper [Paper link]
The code for paper Self-supervised contrastive learning on attribute and topology graphs for predicting relationships among lncRNAs, miRNAs and diseases". The repository is organized as follows:
data/
contains the dataset 1 and dataset 2 used in the paper, with dataset 1 as an example;
lnc(mi)_dis_association_new2.txt
and lnc_mi_interaction_new2.txt
contain known lncRNA(miRNA)-disease associations and lncRNA-miRNA interactions, respectively;LDA/MDA/LMI.edgelist
contain known LDA, MDA, and LMI pairs, respectively; no_LDA/MDA/LMI.edgelist
contain unknown LDA, MDA, LMI pairs;lncRNA/miRNA_sequences2.xlsx
contain lncRNA and miRNA sequences, lncRNA sequences are from NCBI, miRNA sequences are from miRBase;disease_name.xlsx
contains disease names and their DOID numbers;dis_sem_sim.txt
contains disease semantic similarity data:code/
data_preparation.py
is used to calculate lncRNA/miRNA k-mer features and construct knn graph (attribute graph) of lncRNA/miRNA/disease.calculating_similarity.py
is used to calclulate lncRNA/miRNA/disease GIPK similarities and obtain the intra-edges in the topology graph;parms_setting.py
contains hyperparmeters;utils.py
contains preprocessing function of the data;data_preprocess.py
contains the preprocess of data;layer.py
contains SSCLMD's model layer;train.py
contains training and testing code;main.py
runs SSCLMD;Here we provide a example to predict the lncRNA-disease association scores on dataset 1:
It is recommended that you save the training and test sets for each fold and then calculate the lncRNA/miRNA/disease functional similarity. Then continue with subsequent calculations, which will speed up the calculation.
If you have any questions, please email Nan Sheng (shengnan@jlu.edu.cn)