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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
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.piRNAPredictor
The source code for the paper "A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs",Zhang et al. (2016) 17: 329. doi:10.1186/s12859-016-1206-3EPIHC
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