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GraphCDR
GraphCDR: A graph neural network method with contrastive learning for cancer drug response predictionlncRNA-protein-interaction-prediction
The source codes for our paper submitted to Neurocomputing in 2017. We propose a linear label propagation method (LPLNP method) to predict unknown lncRNA-protein interactions. In this repository, you can find our dataset and code of individual LPLNP, intergrated LPLNP models, and other state-of-the-art methods. Please follow the Guideline.pdf.CSGNN
Disease-Mesh-Similarity
For each disease, a directed acyclic graph (DAG) is constructed based on hierarchical descriptors, in which nodes represent disease MeSH descriptors (or disease terms) and edges represent the relationship between the current node and its ancestors. Then compare the DAG Similarity.TDRC
tensor decomposition, pythonNEMII
PHIAF
SCMFDD
SLNPM
LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combinationSFLLN
The source code for our paper "SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug-drug interactions". In this repository, you can find our dataset "feature matrix.xls", "interaction matrix.txt" and source code "SFLLN.py". Please follow the "Readme.txt" for more details.relationGCN
最基础的gcn实现单关联预测PredLnc-GFStack
PredLnc-GFStack: a Global Sequence Feature Based on a Stacked Ensemble Learning Method for Predicting lncRNAs from TranscriptsBDSILP
ItLnc-BXE
ItLnc-BXE: Identification of plant lncRNAs using a Bagging-XGBoost-ensemble method with multiple featuresLncPred-IEL
LncPred-IEL: A Long Non-coding RNA Prediction Method using Iterative Ensemble LearningsRNA-prediction
Code and dataset for "Sequence-based bacterial small RNAs prediction using ensemble learning strategies" Please kindly cite the paper if you use the code or the datasets.CD-LNLP
Datasets and Code for "Predicting CircRNA-disease Associations through Linear Neighborhood Label Propagation Method"drug-drug-interaction
The source code for the paper "Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data",Zhang et al. BMC Bioinformatics (2017) 18:18.EPIHC
EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learningDeepiRNA
data and source codes for paper "DeepiRNA: Predicting transposon-derived piRNAs with deep learning"Love Open Source and this site? Check out how you can help us