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compareexperiment

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.

dch-gcn

deepkegg

DeepKEGG

drgcncda

dsinmf

gamclda

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

ganlda

The implementation of “GANLDA: Graph attention network for lncRNA-disease associations prediction”

ibpgnet

IBPGNET

ignscda

We propose a new computational model based on improved graph convolutional network and negative sampling to predict circRNA-disease associations.

jlonmfsc

code for paper JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering

kaatcda

kgancda

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.

ldicdl

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

lgcda

lsgmc

Multiview Subspace Clustering via Low-rank Consistency and Symmetric Affinity Graph

lsgmc-main

mnv-mc

mulgonet

MULGONET

necma

The implementation of “Prediction of circRNA-miRNA associations based on Network Embedding”, Wei Lan, Mingrui Zhu, Qingfeng Chen, Jianwei Chen, Jin Ye, Jin Liu

scahvc

sccl

sciac

scmomtf