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Hypergraph models of biological networks to identify genes critical to pathogenic viral response
Hypergraph models of biological networks to identify genes critical to pathogenic viral response
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Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Feng, Song ; Heath, Emily ; Jefferson, Brett ; Joslyn, Cliff ; Kvinge, Henry ; Mitchell, Hugh D. ; Praggastis, Brenda ; Eisfeld, Amie J. ; Sims, Amy C. ; Thackray, Larissa B. ; Fan, Shufang ; Walters, Kevin B. ; Halfmann, Peter J. ; Westhoff-Smith, Danielle ; Tan, Qing ; Menachery, Vineet D. ; Sheahan, Timothy P. ; Cockrell, Adam S. ; Kocher, Jacob F. ; Stratton, Kelly G. ; Heller, Natalie C. ; Bramer, Lisa M. ; Diamond, Michael S. ; Baric, Ralph S. ; Waters, Katrina M. ; Kawaoka, Yoshihiro ; McDermott, Jason E. ; Purvine, Emilie

BMC bioinformatics, 2021-05, Vol.22 (1), p.1-287, Article 287 [Periódico revisado por pares]

London: BioMed Central

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2
GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification
GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification
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GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

Hu, Jinlong ; Cao, Lijie ; Li, Tenghui ; Dong, Shoubin ; Li, Ping

BMC bioinformatics, 2021-07, Vol.22 (1), p.1-379, Article 379 [Periódico revisado por pares]

London: BioMed Central Ltd

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3
Pan-genome de Bruijn graph using the bidirectional FM-index
Pan-genome de Bruijn graph using the bidirectional FM-index
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Pan-genome de Bruijn graph using the bidirectional FM-index

Depuydt, Lore ; Renders, Luca ; Abeel, Thomas ; Fostier, Jan

BMC bioinformatics, 2023-10, Vol.24 (1), p.1-400, Article 400 [Periódico revisado por pares]

London: BioMed Central

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4
METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs
METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs
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METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

Zhang, Zhenmiao ; Zhang, Lu

BMC bioinformatics, 2021-07, Vol.22 (Suppl 10), p.1-378, Article 378 [Periódico revisado por pares]

London: BioMed Central Ltd

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5
Directed acyclic graphs and causal thinking in clinical risk prediction modeling
Directed acyclic graphs and causal thinking in clinical risk prediction modeling
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Directed acyclic graphs and causal thinking in clinical risk prediction modeling

Piccininni, Marco ; Konigorski, Stefan ; Rohmann, Jessica L ; Kurth, Tobias

BMC medical research methodology, 2020-07, Vol.20 (1), p.179-179, Article 179 [Periódico revisado por pares]

England: BioMed Central Ltd

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6
GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs
GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs
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GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Zeng, Xin ; Meng, Fan-Fang ; Wen, Meng-Liang ; Li, Shu-Juan ; Li, Yi

BMC genomics, 2024-05, Vol.25 (1), p.406-406, Article 406 [Periódico revisado por pares]

England: BioMed Central Ltd

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7
Bipartite graphs in systems biology and medicine: a survey of methods and applications
Bipartite graphs in systems biology and medicine: a survey of methods and applications
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Bipartite graphs in systems biology and medicine: a survey of methods and applications

Pavlopoulos, Georgios A ; Kontou, Panagiota I ; Pavlopoulou, Athanasia ; Bouyioukos, Costas ; Markou, Evripides ; Bagos, Pantelis G

Gigascience, 2018-04, Vol.7 (4), p.1-31 [Periódico revisado por pares]

United States: Oxford University Press

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8
Drug-target interaction prediction using semi-bipartite graph model and deep learning
Drug-target interaction prediction using semi-bipartite graph model and deep learning
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Drug-target interaction prediction using semi-bipartite graph model and deep learning

Eslami Manoochehri, Hafez ; Nourani, Mehrdad

BMC bioinformatics, 2020-07, Vol.21 (Suppl 4), p.1-16, Article 248 [Periódico revisado por pares]

London: BioMed Central Ltd

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9
Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
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Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

Liang, Bilin ; Gong, Haifan ; Lu, Lu ; Xu, Jie

BMC bioinformatics, 2022-09, Vol.23 (1), p.1-394, Article 394 [Periódico revisado por pares]

London: BioMed Central Ltd

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10
Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions
Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions
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Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

Baranwal, Mayank ; Magner, Abram ; Saldinger, Jacob ; Turali-Emre, Emine S ; Elvati, Paolo ; Kozarekar, Shivani ; VanEpps, J. Scott ; Kotov, Nicholas A ; Violi, Angela ; Hero, Alfred O

BMC bioinformatics, 2022-09, Vol.23 (1), p.1-370, Article 370 [Periódico revisado por pares]

London: BioMed Central Ltd

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