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Profiling and Bioinformatics Analysis of Differentially Expressed circRNAs in Spinal Ligament Tissues of Patients with Ankylosing Spondylitis

BioMed research international, 2020, Vol.2020, p.7165893-12

2020

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Integrated Bioinformatics Analysis Reveals Function and Regulatory Network of miR-200b-3p in Endometriosis

BioMed research international, 2020, Vol.2020, p.3962953-7

2020

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ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Bioinformatics, 2018-12, Vol.34 (23), p.4007-4016

2018

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Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features

BioMed research international, 2020, Vol.2020, p.1475368-12

2020

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DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites

Briefings in bioinformatics, 2021-05, Vol.22 (3)

2021

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iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

Bioinformatics, 2018-07, Vol.34 (14), p.2499-2502

2018

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DiCleave: a deep learning model for predicting human Dicer cleavage sites

BMC bioinformatics, 2024-01, Vol.25 (1), p.13-13, Article 13

2024

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PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

BMC bioinformatics, 2022-10, Vol.23 (1), p.1-444, Article 444

2022

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hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties

Biochimica et biophysica acta, 2013-08, Vol.1834 (8), p.1461-1467

2013

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An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP

Molecular therapy. Nucleic acids, 2020-12, Vol.22, p.362-372

2020

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iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

Briefings in bioinformatics, 2020-05, Vol.21 (3), p.1047-1057

2020

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DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

Bioinformatics, 2020-02, Vol.36 (4), p.1057-1065

2020

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Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features

Briefings in bioinformatics, 2015-07, Vol.16 (4), p.640-657

2015

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iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Briefings in bioinformatics, 2019-03, Vol.20 (2), p.638-658

2019

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MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters

Bioinformatics (Oxford, England), 2019-09, Vol.35 (17), p.2957-2965

2019

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POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles

Bioinformatics (Oxford, England), 2017-09, Vol.33 (17), p.2756-2758

2017

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GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome

Bioinformatics, 2015-05, Vol.31 (9), p.1411-1419

2015

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Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome

Bioinformatics, 2018-12, Vol.34 (24), p.4223-4231

2018

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Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.2126-2140

2021

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A polytherapy based approach to combat antimicrobial resistance using cubosomes

Nature communications, 2022-01, Vol.13 (1), p.343-343, Article 343

2022

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PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

Journal of theoretical biology, 2018-04, Vol.443, p.125-137

2018

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A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Briefings in bioinformatics, 2020-07, Vol.21 (4), p.1119-1135

2020

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Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling

IEEE/ACM transactions on computational biology and bioinformatics, 2021-09, Vol.18 (5), p.1937-1945

2021

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IEEE_Xplore

Positive-unlabelled learning of glycosylation sites in the human proteome

BMC bioinformatics, 2019-03, Vol.20 (1), p.112-112, Article 112

2019

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Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors

Bioinformatics, 2018-08, Vol.34 (15), p.2546-2555

2018

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Profiling and Bioinformatics Analysis of Differentially Expressed circRNAs in Spinal Ligament Tissues of Patients with Ankylosing Spondylitis

Integrated Bioinformatics Analysis Reveals Function and Regulatory Network of miR-200b-3p in Endometriosis

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features

DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

DiCleave: a deep learning model for predicting human Dicer cleavage sites

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties

An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters

POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles

GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework

A polytherapy based approach to combat antimicrobial resistance using cubosomes

PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling

Positive-unlabelled learning of glycosylation sites in the human proteome

Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors

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