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3Drefine: an interactive web server for efficient protein structure refinement

Nucleic acids research, 2016-07, Vol.44 (W1), p.W406-W409

2016

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Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Proteins, structure, function, and bioinformatics, 2019-12, Vol.87 (12), p.1165-1178

2019

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DeepSF: deep convolutional neural network for mapping protein sequences to folds

Bioinformatics, 2018-04, Vol.34 (8), p.1295-1303

2018

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CONFOLD2: improved contact-driven ab initio protein structure modeling

BMC bioinformatics, 2018-01, Vol.19 (1), p.22-22, Article 22

2018

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De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Nature communications, 2024-06, Vol.15 (1), p.5511-13, Article 5511

2024

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3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

Proteins, structure, function, and bioinformatics, 2013-01, Vol.81 (1), p.119-131

2013

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Predicting protein residue―residue contacts using deep networks and boosting

Bioinformatics, 2012-12, Vol.28 (23), p.3066-3072

2012

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DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

BMC bioinformatics, 2021-01, Vol.22 (1), p.30-30, Article 30

2021

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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

International journal of molecular sciences, 2020-04, Vol.21 (8), p.2873

2020

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Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Nature communications, 2022-11, Vol.13 (1), p.6963-6963, Article 6963

2022

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DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

BMC bioinformatics, 2022-04, Vol.23 (Suppl 3), p.141-141, Article 141

2022

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A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

IEEE/ACM transactions on computational biology and bioinformatics, 2015-01, Vol.12 (1), p.103-112

2015

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VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data

Scientific reports, 2021-04, Vol.11 (1), p.8880-8880, Article 8880

2021

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ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data

BMC bioinformatics, 2017-11, Vol.18 (1), p.480-480, Article 480

2017

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Improving Protein Fold Recognition by Deep Learning Networks

Scientific reports, 2015-12, Vol.5 (1), p.17573-17573, Article 17573

2015

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A machine learning information retrieval approach to protein fold recognition

Bioinformatics, 2006-06, Vol.22 (12), p.1456-1463

2006

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FeOOH decorated CoP porous nanofiber for enhanced oxygen evolution activity

Chemical engineering journal (Lausanne, Switzerland : 1996), 2022-01, Vol.428, p.131130, Article 131130

2022

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A deep auto-encoder model for gene expression prediction

BMC genomics, 2017-11, Vol.18 (Suppl 9), p.845-845, Article 845

2017

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Four-Dimensional Chromosome Structure Prediction

International journal of molecular sciences, 2021-09, Vol.22 (18), p.9785

2021

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Improved residue contact prediction using support vector machines and a large feature set

BMC bioinformatics, 2007-04, Vol.8 (1), p.113-113, Article 113

2007

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Genome sequence of the palaeopolyploid soybean

Nature (London), 2010-01, Vol.463 (7278), p.178-183

2010

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Analysis of several key factors influencing deep learning-based inter-residue contact prediction

Bioinformatics, 2020-02, Vol.36 (4), p.1091-1098

2020

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A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data

BMC genomics, 2018-02, Vol.19 (1), p.161-161, Article 161

2018

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Prediction of protein stability changes for single-site mutations using support vector machines

Proteins, structure, function, and bioinformatics, 2006-03, Vol.62 (4), p.1125-1132

2006

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Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Biomolecules (Basel, Switzerland), 2023-01, Vol.13 (1), p.132

2023

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3Drefine: an interactive web server for efficient protein structure refinement

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

DeepSF: deep convolutional neural network for mapping protein sequences to folds

CONFOLD2: improved contact-driven ab initio protein structure modeling

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

Predicting protein residue―residue contacts using deep networks and boosting

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data

Improving Protein Fold Recognition by Deep Learning Networks

A machine learning information retrieval approach to protein fold recognition

FeOOH decorated CoP porous nanofiber for enhanced oxygen evolution activity

A deep auto-encoder model for gene expression prediction

Four-Dimensional Chromosome Structure Prediction

Improved residue contact prediction using support vector machines and a large feature set

Genome sequence of the palaeopolyploid soybean

Analysis of several key factors influencing deep learning-based inter-residue contact prediction

A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data

Prediction of protein stability changes for single-site mutations using support vector machines

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

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