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This is the code and the data of a underview paper named "Benchmarking computational methods for predicting circRNA-disease asssociations", which contains 10 methods and 5 collected datasets. The detail will be introduced after the paper is accepted.
DeepKEGG
The implementation of “Inferring LncRNA-disease Associations Based On Graph Autoencoder Matrix Completion”, Ximin Wu,Wei Lan,Qingfeng Chen,Yi Dong,Wei Peng,Jin Liu,Jianxin Wang
The implementation of “GANLDA: Graph attention network for lncRNA-disease associations prediction”
IBPGNET
We propose a new computational model based on improved graph convolutional network and negative sampling to predict circRNA-disease associations.
code for paper JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering
KGANCDA is effective to predict associations between circRNA and cancer, which is based on knowledge graph attention network. The detail of the paper will be released in this page after the paper accepted.
The implementation of “LDICDL: LncRNA-disease interaction identification based on Collaborative Deep Learning”, Wei Lan, Dehuang Lai, Qingfeng Chen, Ximin Wu, Baoshan Chen, Jianxin Wang, and Yi-Ping Phoebe Chen
Multiview Subspace Clustering via Low-rank Consistency and Symmetric Affinity Graph
MULGONET
The implementation of “Prediction of circRNA-miRNA associations based on Network Embedding”, Wei Lan, Mingrui Zhu, Qingfeng Chen, Jianwei Chen, Jin Ye, Jin Liu
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