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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Data mining and knowledge discovery, 2020-09, Vol.34 (5), p.1454-1495

2020

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Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.523

2019

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iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Nucleic acids research, 2021-06, Vol.49 (10), p.e60-e60

2021

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InceptionTime: Finding AlexNet for time series classification

Data mining and knowledge discovery, 2020-11, Vol.34 (6), p.1936-1962

2020

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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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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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Proximity Forest: an effective and scalable distance-based classifier for time series

Data mining and knowledge discovery, 2019-05, Vol.33 (3), p.607-635

2019

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SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting

Machine learning, 2023-07, Vol.112 (7), p.2555-2591

2023

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PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites

PloS one, 2012-11, Vol.7 (11), p.e50300

2012

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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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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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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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Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Briefings in bioinformatics, 2019-11, Vol.20 (6), p.2267-2290

2019

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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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Positive-unlabeled learning in bioinformatics and computational biology: a brief review

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

2022

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PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

Scientific reports, 2017-07, Vol.7 (1), p.6862-19, Article 6862

2017

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Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Genomics, proteomics & bioinformatics, 2020-02, Vol.18 (1), p.52-64

2020

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Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods

Briefings in bioinformatics, 2019-11, Vol.20 (6), p.2150-2166

2019

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SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

BMC bioinformatics, 2019-11, Vol.20 (1), p.602-12, Article 602

2019

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Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features

Scientific reports, 2014-07, Vol.4 (1), p.5765, Article 5765

2014

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A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

IEEE/ACM transactions on computational biology and bioinformatics, 2021-09, Vol.18 (5), p.1801-1810

2021

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Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis

PloS one, 2017-12, Vol.12 (12), p.e0188688-e0188688

2017

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Cascleave: towards more accurate prediction of caspase substrate cleavage sites

Bioinformatics, 2010-03, Vol.26 (6), p.752-760

2010

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Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Machine learning, 2012-02, Vol.86 (2), p.233-272

2012

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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

InceptionTime: Finding AlexNet for time series classification

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

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

Proximity Forest: an effective and scalable distance-based classifier for time series

SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting

PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites

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

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

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

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Positive-unlabelled learning of glycosylation sites in the human proteome

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods

SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis

Cascleave: towards more accurate prediction of caspase substrate cleavage sites

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

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