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Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC)

Biotechnology and bioengineering, 2021-11, Vol.118 (11), p.4204-4216

2021

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A computational platform to identify origins of replication sites in eukaryotes

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.1940-1950

2021

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Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method

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

2021

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DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach

Briefings in bioinformatics, 2021-06, Vol.22 (6)

2021

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DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

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

2021

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A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae

Briefings in bioinformatics, 2020-09, Vol.21 (5), p.1568-1580

2020

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DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction

iMeta, 2022-03, Vol.1 (1), p.e11-n/a

2022

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Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

Molecules (Basel, Switzerland), 2018-08, Vol.23 (8), p.2000

2018

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Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique

International journal of molecular sciences, 2022-01, Vol.23 (3), p.1251

2022

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Erratum for “Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC)” (Vol. 118, Issue 11, pp. 4204–4216)

Biotechnology and bioengineering, 2022-04, Vol.119 (4), p.1171-1171

2022

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Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

Briefings in bioinformatics, 2021-11, Vol.22 (6)

2021

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Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Molecules (Basel, Switzerland), 2017-06, Vol.22 (7), p.1057

2017

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iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

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

2021

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Computational identification of N6-methyladenosine sites in multiple tissues of mammals

Computational and structural biotechnology journal, 2020-01, Vol.18, p.1084-1091

2020

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A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

Current drug targets, 2019-01, Vol.20 (5), p.540-550

2019

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iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes

iScience, 2020-04, Vol.23 (4), p.100991-100991, Article 100991

2020

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iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice

Frontiers in genetics, 2019-09, Vol.10, p.793-793

2019

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iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice

Briefings in bioinformatics, 2022-01, Vol.23 (1)

2022

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iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Frontiers in microbiology, 2022-02, Vol.13, p.790063-790063

2022

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Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Frontiers in bioengineering and biotechnology, 2020-03, Vol.8, p.254-254

2020

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Identification of cyclin protein using gradient boost decision tree algorithm

Computational and structural biotechnology journal, 2021-01, Vol.19, p.4123-4131

2021

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Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

Research (Washington), 2022, Vol.2022, p.9780293-9780293

2022

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Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method

Frontiers in cell and developmental biology, 2020-10, Vol.8, p.582864-582864

2020

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Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique

Bioinformatics (Oxford, England), 2019-06, Vol.35 (12), p.2075-2083

2019

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A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation

Molecular therapy. Nucleic acids, 2021-03, Vol.23, p.347-354

2021

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Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC)

A computational platform to identify origins of replication sites in eukaryotes

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method

DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae

DeepKla: An attention mechanism‐based deep neural network for protein lysine lactylation site prediction

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique

Erratum for “Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC)” (Vol. 118, Issue 11, pp. 4204–4216)

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes

iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice

iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Identification of cyclin protein using gradient boost decision tree algorithm

Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method

Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique

A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation

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