Journal Article10.1016/J.COMPBIOLCHEM.2020.107282
Inferring LncRNA-disease associations based on graph autoencoder matrix completion.
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TL;DR: A computational framework based on graph autoencoder matrix completion (GAMCLDA) to identify lncRNA-disease associations and outperforms other state-of-the-art methods in prediction performance which is evaluated by AUC value, AUPR value, PPV and F1-score.
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About: This article is published in Computational Biology and Chemistry. The article was published on 01 Aug 2020. The article focuses on the topics: Autoencoder & Graph (abstract data type).
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Citations
LDICDL: LncRNA-disease association identification based on Collaborative Deep Learning.
Wei Lan,Dehuan Lai,Qingfeng Chen,Ximin Wu,Baoshan Chen,Jin Liu,Jianxin Wang,Yi-Ping Phoebe Chen +7 more
TL;DR: This study proposes a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning that outperforms than other state-of-the-art methods in prediction performance.
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A comprehensive survey on computational methods of non-coding RNA and disease association prediction
TL;DR: In this paper, a survey on the relationship between non-coding RNAs and diseases is presented, which mainly introduces three common non-coders (i.e., miRNAs, lncRNAs and circRNAs) and related computational methods for predicting their association with diseases.
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MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model
TL;DR: Zhang et al. as discussed by the authors proposed a novel model named MAGCNSE to predict underlying lncRNA-disease associations, which first obtained multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network.
LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder
TL;DR: A novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder based on the global self-attention mechanism is proposed and outperforms the state-of-the-art baseline methods.
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GANLDA: Graph attention network for lncRNA-disease associations prediction
01 Jan 2022
TL;DR: In this paper , an end-to-end computational model based on graph attention network (GANLDA) is proposed to predict associations between long non-coding RNAs and diseases.
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