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Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.
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Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival
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Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival
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Computational tools for prioritizing candidate genes: boosting disease gene discovery
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Disease ontology: a backbone for disease semantic integration
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A network diffusion model of disease progression in dementia
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Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko · 2013
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Going the distance for protein function prediction: a new distance metric for protein interaction networks
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An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
A. Machens, F. Gesualdo, C. Rizzo, A. E. Tozzi, A. Barrat, and C. Cattuto · 2013
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A survey of frequent subgraph mining algorithms
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Going the distance for protein function prediction: a new distance metric for protein interaction networks
Cao, M. et al · 2013
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Deepwalk: online learning of social representations
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Synergistic and antagonistic drug combinations depend on network topology
N. Yin, W. Ma, J. Pei, Q. Ouyang, C. Tang, and L. Lai · 2014
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Auto-encoding variational bayes
Kingma, D. P. & Welling, M · 2014
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Long-range chromatin interactions
J. Dekker and T. Misteli · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Uncovering disease-disease relationships through the incomplete interactome
J. Menche, A. Sharma, M. Kitsak, S. D. Ghiassian, M. Vidal, J. Loscalzo, and A.-L. Barabási · 2015
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LINE: Large-scale information network embedding
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Embedding entities and relations for learning and inference in knowledge bases
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Gene prioritization by compressive data fusion and chaining
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Link prediction methods and their accuracy for different social networks and network metrics
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Head and neck cancer subtypes with biological and clinical relevance: Meta-analysis of gene-expression data
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Uncovering disease-disease relationships through the incomplete interactome
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A multimodal data analysis approach for targeted drug discovery involving topological data analysis (tda)
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Network Science
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Convolutional neural networks on graphs with fast localized spectral filtering
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node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
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Network-based in silico drug efficacy screening
E. Guney, J. Menche, M. Vidal, and A.-L. Barábasi · 2016
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Network biology concepts in complex disease comorbidities
J. X. Hu, C. E. Thomas, and S. Brunak · 2016
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Molecular graph convolutions: moving beyond fingerprints
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley · 2016
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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Ontology-based disease similarity network for disease gene prediction
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Methods for causal inference from gene perturbation experiments and validation
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Complex embeddings for simple link prediction
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard · 2016
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Order matters: Sequence to sequence for sets
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Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function
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Systematic functional annotation and visualization of biological networks
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Variational graph auto-encoders
Kipf, T. N. & Welling, M · 2016
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GRAM: graph-based attention model for healthcare representation learning
E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun · 2017
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Network propagation: a universal amplifier of genetic associations
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metapath2vec: Scalable representation learning for heterogeneous networks
Y. Dong, N. V. Chawla, and A. Swami · 2017
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Protein interface prediction using graph convolutional networks
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Neural message passing for quantum chemistry
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Representation learning on graphs: Methods and applications
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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The monarch initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
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Single-cell topological rna-seq analysis reveals insights into cellular differentiation and development
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Learning a health knowledge graph from electronic medical records
M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, and D. Sontag · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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Simultaneous epitope and transcriptome measurement in single cells
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Predicting multicellular function through multi-layer tissue networks
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Deep learning with topological signatures
Hofer, C. D., Kwitt, R., Niethammer, M. & Uhl, A · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
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Large-scale analysis of disease pathways in the human interactome
M. Agrawal, M. Zitnik, and J. Leskovec · 2018
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Next-generation machine learning for biological networks
D. M. Camacho, K. M. Collins, R. K. Powers, J. C. Costello, and J. J. Collins · 2018
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Network-based approach to prediction and population-based validation of in silico drug repurposing
F. Cheng, R. J. Desai, D. E. Handy, R. Wang, S. Schneeweiss, A.-L. Barabási, and J. Loscalzo · 2018
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Protein classification with improved topological data analysis
T. K. Dey and S. Mandal · 2018
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Towards gene expression convolutions using gene interaction graphs
F. Dutil, J. P. Cohen, M. Weiss, G. Derevyanko, and Y. Bengio · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
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Heteromed: Heterogeneous information network for medical diagnosis
A. Hosseini, T. Chen, W. Wu, Y. Sun, and M. Sarrafzadeh · 2018
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Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. S. Jaakkola · 2018
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KAME: knowledge-based attention model for diagnosis prediction in healthcare
F. Ma, Q. You, H. Xiao, R. Chitta, J. Zhou, and J. Gao · 2018
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Drug similarity integration through attentive multi-view graph auto-encoders
T. Ma, C. Xiao, J. Zhou, and F. Wang · 2018
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Supervised approaches for protein function prediction by topological data analysis
A. Martino, A. Rizzi, and F. M. F. Mascioli · 2018
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Network spectra for drug-target identification in complex diseases: new guns against old foes
A. Rai, P. Shinde, and S. Jalan · 2018
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Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification
S. Rhee, S. Seo, and S. Kim · 2018
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
M. Simonovsky and N. Komodakis · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Graphgan: Graph representation learning with generative adversarial nets
H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
S. Yan, Y. Xiong, and D. Lin · 2018
Graph representation learning
W. L. Hamilton · 2020
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Predicting gene expression from network topology using graph neural networks
R. Hasibi and T. Michoel · 2020
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Strategies for pre-training graph neural networks
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. S. Pande, and J. Leskovec · 2020
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Heterogeneous graph transformer
Z. Hu, Y. Dong, K. Wang, and Y. Sun · 2020
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scGNN: scrna-seq dropout imputation via induced hierarchical cell similarity graph
K. Huang · 2020
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SkipGNN: predicting molecular interactions with skip-graph networks
K. Huang, C. Xiao, L. M. Glass, M. Zitnik, and J. Sun · 2020
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Hergepred: heterogeneous network embedding representation for disease gene prediction
K. Yang, R. Wang, G. Liu, Z. Shu, N. Wang, R. Zhang, J. Yu, J. Chen, X. Li, and X. Zhou · 2018
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Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
J. You, R. Ying, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
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Emr-based medical knowledge representation and inference via markov random fields and distributed representation learning
C. Zhao, J. Jiang, Y. Guan, X. Guo, and B. He · 2018
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Feature engineering for machine learning: principles and techniques for data scientists
A. Zheng and A. Casari · 2018
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Graph meta learning via local subgraphs
K. Huang and M. Zitnik · 2020
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M. Jiang, Z. Li, S. Zhang, S. Wang, X. Wang, Q. Yuan, and Z. Wei · 2020
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W. Jin, R. Barzilay, and T. Jaakkola · 2020
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Graph neural network-based diagnosis prediction
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Computational network biology: Data, models, and applications
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A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction
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Deep learning of high-order interactions for protein interface prediction
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A reference map of the human binary protein interactome
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A topological data analysis approach on predicting phenotypes from gene expression data
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Transforming the language of life: Transformer neural networks for protein prediction tasks
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Constructing knowledge graphs and their biomedical applications
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Evolvegcn: Evolving graph convolutional networks for dynamic graphs
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Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification
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Inferring causal gene regulatory networks from coupled single-cell expression dynamics using scribe
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Classification of cancer types using graph convolutional neural networks
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Disease state prediction from single-cell data using graph attention networks
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Temporal graph networks for deep learning on dynamic graphs
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A deep learning approach to antibiotic discovery
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Disease prediction via graph neural networks
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Dtigems+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
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Where are the disease-associated eqtls?
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Mipdh: A novel computational model for predicting microRNA–mRNA interactions by DeepWalk on a heterogeneous network
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A comprehensive survey on graph neural networks
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Graph-based prediction of protein-protein interactions with attributed signed graph embedding
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Denoising protein-protein interaction network via variational graph auto-encoder for protein complex detection
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When does self-supervision help graph convolutional networks?
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Gcng: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
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Graph embedding on biomedical networks: methods, applications and evaluations
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Graphsaint: Graph sampling based inductive learning method
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GNNGuard: Defending graph neural networks against adversarial attacks
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The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications
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Deep learning on graphs: A survey
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Computational network biology: Data, models, and applications
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