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DeepDTAF: a deep learning method to predict protein-ligand binding affinity

Briefings in bioinformatics, 2021-09, Vol.22 (5)

2021

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BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction

Bioinformatics, 2022-03, Vol.38 (7), p.1995-2002

2022

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Prediction of lncRNA-disease associations based on inductive matrix completion

Bioinformatics, 2018-10, Vol.34 (19), p.3357-3364

2018

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Computational drug repositioning based on multi-similarities bilinear matrix factorization

Briefings in bioinformatics, 2021-07, Vol.22 (4)

2021

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Biomedical data and computational models for drug repositioning: a comprehensive review

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.1604-1619

2021

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DeepEP: a deep learning framework for identifying essential proteins

BMC bioinformatics, 2019-12, Vol.20 (Suppl 16), p.506-506, Article 506

2019

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Clinical big data and deep learning: Applications, challenges, and future outlooks

Big Data Mining and Analytics, 2019-12, Vol.2 (4), p.288-305

2019

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DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

BMC bioinformatics, 2020-11, Vol.21 (Suppl 6), p.203-203, Article 203

2020

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Overlap matrix completion for predicting drug-associated indications

PLoS computational biology, 2019-12, Vol.15 (12), p.e1007541-e1007541

2019

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A survey of matrix completion methods for recommendation systems

Big Data Mining and Analytics, 2018-12, Vol.1 (4), p.308-323

2018

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A breakdown-free block conjugate gradient method

BIT, 2017-06, Vol.57 (2), p.379-403

2017

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scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Nature communications, 2024-08, Vol.15 (1), p.7561-20, Article 7561

2024

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HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation

Nature communications, 2024-07, Vol.15 (1), p.5573-14, Article 5573

2024

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Block Conjugate Gradient algorithms for least squares problems

Journal of computational and applied mathematics, 2017-06, Vol.317, p.203-217

2017

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Identifying At-Risk Students for Early Interventions-A Time-Series Clustering Approach

IEEE transactions on emerging topics in computing, 2017-01, Vol.5 (1), p.45-55

2017

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Conformational sampling in template-free protein loop structure modeling: an overview

Computational and structural biotechnology journal, 2013-02, Vol.5 (6), p.e201302003-e201302003, Article e201302003

Li, Yaohang

2013

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Big data-enabled multiscale serviceability analysis for aging bridges

Digital communications and networks, 2016-08, Vol.2 (3), p.97-107

2016

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Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework

Communications biology, 2023-08, Vol.6 (1), p.870-870, Article 870

2023

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Building a Knowledge-Based Statistical Potential by Capturing High-Order Inter-residue Interactions and its Applications in Protein Secondary Structure Assessment

Journal of chemical information and modeling, 2013-02, Vol.53 (2), p.500-508

2013

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Context-Based Features Enhance Protein Secondary Structure Prediction Accuracy

Journal of chemical information and modeling, 2014-03, Vol.54 (3), p.992-1002

2014

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Drug repositioning based on bounded nuclear norm regularization

Bioinformatics, 2019-07, Vol.35 (14), p.i455-i463

2019

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Sampling Multiple Scoring Functions Can Improve Protein Loop Structure Prediction Accuracy

Journal of chemical information and modeling, 2011-07, Vol.51 (7), p.1656-1666

2011

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Backbone Statistical Potential from Local Sequence-Structure Interactions in Protein Loops

The journal of physical chemistry. B, 2010-02, Vol.114 (5), p.1859-1869

2010

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A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals

Bioinformatics (Oxford, England), 2021-09, Vol.37 (18), p.2841-2847

2021

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Protein–protein interaction site prediction through combining local and global features with deep neural networks

Bioinformatics, 2020-02, Vol.36 (4), p.1114-1120

2020

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DeepDTAF: a deep learning method to predict protein-ligand binding affinity

BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction

Prediction of lncRNA-disease associations based on inductive matrix completion

Computational drug repositioning based on multi-similarities bilinear matrix factorization

Biomedical data and computational models for drug repositioning: a comprehensive review

DeepEP: a deep learning framework for identifying essential proteins

Clinical big data and deep learning: Applications, challenges, and future outlooks

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

Overlap matrix completion for predicting drug-associated indications

A survey of matrix completion methods for recommendation systems

A breakdown-free block conjugate gradient method

scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation

Block Conjugate Gradient algorithms for least squares problems

Identifying At-Risk Students for Early Interventions-A Time-Series Clustering Approach

Conformational sampling in template-free protein loop structure modeling: an overview

Big data-enabled multiscale serviceability analysis for aging bridges

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework

Building a Knowledge-Based Statistical Potential by Capturing High-Order Inter-residue Interactions and its Applications in Protein Secondary Structure Assessment

Context-Based Features Enhance Protein Secondary Structure Prediction Accuracy

Drug repositioning based on bounded nuclear norm regularization

Sampling Multiple Scoring Functions Can Improve Protein Loop Structure Prediction Accuracy

Backbone Statistical Potential from Local Sequence-Structure Interactions in Protein Loops

A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals

Protein–protein interaction site prediction through combining local and global features with deep neural networks

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