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Journal Article

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac361, https://doi.org/10.1093/bib/bbac361

Published: 10 September 2022

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Journal Article

multi-type neighbors enhanced global topology and pairwise attribute learning for drug–protein interaction prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac120, https://doi.org/10.1093/bib/bbac120

Published: 05 May 2022

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Journal Article

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 3, May 2022, bbac126, https://doi.org/10.1093/bib/bbac126

Published: 25 April 2022

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Journal Article

Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 3, May 2022, bbac123, https://doi.org/10.1093/bib/bbac123

Published: 08 April 2022

Supplementary data

Journal Article

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 3, May 2022, bbac089, https://doi.org/10.1093/bib/bbac089

Published: 31 March 2022

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Journal Article

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug–disease association prediction

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Hongda Zhang and others

Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbac009, https://doi.org/10.1093/bib/bbac009

Published: 07 February 2022

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Journal Article

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction

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Kaimiao Hu and others

Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab606, https://doi.org/10.1093/bib/bbab606

Published: 02 February 2022

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Journal Article

Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets

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Ling Gao and others

Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab467, https://doi.org/10.1093/bib/bbab467

Published: 30 November 2021

Supplementary data

Journal Article

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab453, https://doi.org/10.1093/bib/bbab453

Published: 27 October 2021

Supplementary data

Journal Article

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA–disease association prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab428, https://doi.org/10.1093/bib/bbab428

Published: 09 October 2021

Supplementary data

Journal Article

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction

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Ping Xuan and others

Briefings in Bioinformatics, Volume 22, Issue 5, September 2021, bbab119, https://doi.org/10.1093/bib/bbab119

Published: 12 April 2021

Supplementary data

Journal Article

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction

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Nan Sheng and others

Briefings in Bioinformatics, Volume 22, Issue 3, May 2021, bbaa067, https://doi.org/10.1093/bib/bbaa067

Published: 23 May 2020

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Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs

multi-type neighbors enhanced global topology and pairwise attribute learning for drug–protein interaction prediction

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction

Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug–disease association prediction

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction

Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA–disease association prediction

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction

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Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs Free

multi-type neighbors enhanced global topology and pairwise attribute learning for drug–protein interaction prediction Free

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction Free

Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction Free

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs Free

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug–disease association prediction Free

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction Free

Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets Free

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Free

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA–disease association prediction Free

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction Free

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction Free

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This Feature Is Available To Subscribers Only

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs

multi-type neighbors enhanced global topology and pairwise attribute learning for drug–protein interaction prediction

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction

Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug–disease association prediction

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction

Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA–disease association prediction

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction