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Research in Computational Molecular Biology: 23rd Annual International Conference, RECOMB 2019, Washington, DC, USA, May 5-8, 2019, Proceedings

1st ed. 2019., 2019

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Network propagation: a universal amplifier of genetic associations

Nature reviews. Genetics, 2017-09, Vol.18 (9), p.551-562

2017

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Graph embedding on biomedical networks: methods, applications and evaluations

Bioinformatics, 2020-02, Vol.36 (4), p.1241-1251

2020

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deepDR: a network-based deep learning approach to in silico drug repositioning

Bioinformatics (Oxford, England), 2019-12, Vol.35 (24), p.5191-5198

2019

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DeepGOPlus: improved protein function prediction from sequence

Bioinformatics, 2020-01, Vol.36 (2), p.422-429

2020

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Going the distance for protein function prediction: a new distance metric for protein interaction networks

PloS one, 2013-10, Vol.8 (10), p.e76339-e76339

2013

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Discovering protein drug targets using knowledge graph embeddings

Bioinformatics, 2020-01, Vol.36 (2), p.603-610

2020

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ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python

Bioinformatics (Oxford, England), 2021-10, Vol.37 (20), p.3657-3659

2021

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Automatic local resolution-based sharpening of cryo-EM maps

Bioinformatics, 2020-02, Vol.36 (3), p.765-772

2020

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AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer

Bioinformatics (Oxford, England), 2023-01, Vol.39 (1)

2023

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Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

Bioinformatics (Oxford, England), 2023-07, Vol.39 (7)

2023

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COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

Bioinformatics, 2021-06, Vol.37 (9), p.1332-1334

2021

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DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

Bioinformatics (Oxford, England), 2023-01, Vol.39 (1)

2023

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DeepSurf: a surface-based deep learning approach for the prediction of ligand binding sites on proteins

Bioinformatics, 2021-07, Vol.37 (12), p.1681-1690

2021

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DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks

Bioinformatics, 2020-01, Vol.36 (1), p.56-64

2020

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GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

Bioinformatics (Oxford, England), 2020-07, Vol.36 (Supplement_1), p.i464-i473

2020

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PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information

Bioinformatics, 2021-12, Vol.37 (24), p.4668-4676

2021

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Current structure predictors are not learning the physics of protein folding

Bioinformatics, 2022-03, Vol.38 (7), p.1881-1887

2022

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ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks

Bioinformatics, 2019-12, Vol.35 (23), p.4946-4954

2019

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Efficient Construction of a Complete Index for Pan-Genomics Read Alignment

Research in Computational Molecular Biology, 2019, Vol.11467, p.158-173

2019

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GenCLiP 3: mining human genes’ functions and regulatory networks from PubMed based on co-occurrences and natural language processing

Bioinformatics, 2019-11, Vol.36 (6), p.1973-1975

2019

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Predicting protein–peptide binding residues via interpretable deep learning

Bioinformatics, 2022-06, Vol.38 (13), p.3351-3360

2022

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A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks

Bioinformatics, 2020-06, Vol.36 (11), p.3474-3481

2020

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Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks

Bioinformatics, 2020-05, Vol.36 (9), p.2697-2704

2020

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RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature

Research in Computational Molecular Biology, 2019, Vol.11467, p.272-284

2019

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Research in Computational Molecular Biology: 23rd Annual International Conference, RECOMB 2019, Washington, DC, USA, May 5-8, 2019, Proceedings

Network propagation: a universal amplifier of genetic associations

Graph embedding on biomedical networks: methods, applications and evaluations

deepDR: a network-based deep learning approach to in silico drug repositioning

DeepGOPlus: improved protein function prediction from sequence

Going the distance for protein function prediction: a new distance metric for protein interaction networks

Discovering protein drug targets using knowledge graph embeddings

ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python

Automatic local resolution-based sharpening of cryo-EM maps

AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

DeepSurf: a surface-based deep learning approach for the prediction of ligand binding sites on proteins

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information

Current structure predictors are not learning the physics of protein folding

ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks

Efficient Construction of a Complete Index for Pan-Genomics Read Alignment

GenCLiP 3: mining human genes’ functions and regulatory networks from PubMed based on co-occurrences and natural language processing

Predicting protein–peptide binding residues via interpretable deep learning

A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks

RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature

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