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Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification

Computational intelligence and neuroscience, 2022-07, Vol.2022, p.1-14

2022

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Improved AlexNet with Inception-V4 for Plant Disease Diagnosis

Computational intelligence and neuroscience, 2022-09, Vol.2022, p.1-12

2022

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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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PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

Bioinformatics, 2019-11, Vol.35 (21), p.4272-4280

2019

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ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Briefings in bioinformatics, 2020-09, Vol.21 (5), p.1846-1855

2020

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SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis

Proteomics (Weinheim), 2019-09, Vol.19 (17), p.e1900007-n/a

2019

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Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

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

2021

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Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Briefings in bioinformatics, 2020-03, Vol.21 (2), p.408-420

2020

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Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

IEEE/ACM transactions on computational biology and bioinformatics, 2019-07, Vol.16 (4), p.1264-1273

2019

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Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

Briefings in bioinformatics, 2020-01, Vol.21 (1), p.106-119

2020

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mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

Bioinformatics (Oxford, England), 2019-08, Vol.35 (16), p.2757-2765

2019

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Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

International journal of molecular sciences, 2016-12, Vol.17 (12), p.2118-2118

2016

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EP3: an ensemble predictor that accurately identifies type III secreted effectors

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.1918-1928

2021

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Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

IEEE/ACM transactions on computational biology and bioinformatics, 2019-07, Vol.16 (4), p.1231-1239

2019

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M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Molecular therapy. Nucleic acids, 2018-09, Vol.12, p.635-644

2018

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Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

Bioinformatics, 2019-04, Vol.35 (8), p.1326-1333

2019

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SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

BMC genomics, 2017-10, Vol.18 (Suppl 7), p.742-742, Article 742

2017

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MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy

Briefings in bioinformatics, 2020-03, Vol.21 (2), p.687-698

2020

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Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction

Briefings in bioinformatics, 2020-05, Vol.21 (3), p.996-1005

2020

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Iterative feature representations improve N4-methylcytosine site prediction

Bioinformatics, 2019-12, Vol.35 (23), p.4930-4937

2019

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Predicting protein–peptide binding residues via interpretable deep learning

Bioinformatics, 2022-06, Vol.38 (13), p.3351-3360

2022

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Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-14

2022

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Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier

Artificial intelligence in medicine, 2017-11, Vol.83, p.67-74

2017

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ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism

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

2021

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PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model

Computers in biology and medicine, 2023-09, Vol.164, p.107260-107260, Article 107260

2023

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Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification

Improved AlexNet with Inception-V4 for Plant Disease Diagnosis

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

PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

EP3: an ensemble predictor that accurately identifies type III secreted effectors

Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy

Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction

Iterative feature representations improve N4-methylcytosine site prediction

Predicting protein–peptide binding residues via interpretable deep learning

Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier

ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism

PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model

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