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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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Cancer‑associated fibroblast‑derived exosomal miR‑382‑5p promotes the migration and invasion of oral squamous cell carcinoma

Oncology reports, 2019-10, Vol.42 (4), p.1319-1328

2019

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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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Application of an Improved U2-Net Model in Ultrasound Median Neural Image Segmentation

Ultrasound in medicine & biology, 2022-12, Vol.48 (12), p.2512-2520

2022

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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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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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Radiomics for the detection of microvascular invasion in hepatocellular carcinoma

World journal of gastroenterology : WJG, 2022-05, Vol.28 (20), p.2176-2183

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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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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The Influence of Microwave Sterilization on the Ultrastructure, Permeability of Cell Membrane and Expression of Proteins of Bacillus Cereus

Frontiers in microbiology, 2018-09, Vol.9, p.1870-1870

2018

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Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density

Journal of magnetic resonance imaging, 2016-01, Vol.43 (1), p.45-54

2016

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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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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

Cancer‑associated fibroblast‑derived exosomal miR‑382‑5p promotes the migration and invasion of oral squamous cell carcinoma

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

Application of an Improved U2-Net Model in Ultrasound Median Neural Image Segmentation

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

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)

Radiomics for the detection of microvascular invasion in hepatocellular carcinoma

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

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

Recurrent gain-of-function USP8 mutations in Cushing's disease

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

The Influence of Microwave Sterilization on the Ultrastructure, Permeability of Cell Membrane and Expression of Proteins of Bacillus Cereus

Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density

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

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