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1
Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas
Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas
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Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas

The Journal of pathology, 2020-03, Vol.250 (3), p.312 [Peer Reviewed Journal]

ISSN: 1096-9896 ;EISSN: 1096-9896 ;DOI: 10.1002/path.5373

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2
A systematic comparison of supervised classifiers
A systematic comparison of supervised classifiers
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A systematic comparison of supervised classifiers

PloS one, 2014-04, Vol.9 (4), p.e94137-e94137 [Peer Reviewed Journal]

COPYRIGHT 2014 Public Library of Science ;COPYRIGHT 2014 Public Library of Science ;2014 Amancio et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2014 Amancio et al 2014 Amancio et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0094137 ;PMID: 24763312

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3
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
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Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection

Cell Systems, 2020-07, Vol.11 (1), p.11 [Peer Reviewed Journal]

2020. Not withstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.elsevier.com/legal/elsevier-website-terms-and-conditions ;ISSN: 2405-4720 ;ISSN: 2405-4712 ;EISSN: 2405-4720 ;DOI: 10.1016/j.cels.2020.05.012

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4
A performance comparison of eight commercially available automatic classifiers for facial affect recognition
A performance comparison of eight commercially available automatic classifiers for facial affect recognition
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A performance comparison of eight commercially available automatic classifiers for facial affect recognition

PloS one, 2020-04, Vol.15 (4), p.e0231968-e0231968 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Dupré et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Dupré et al 2020 Dupré et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0231968 ;PMID: 32330178

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5
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
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Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

Genome Biology, 2017-09, Vol.18 (1), p.182-19, Article 182 [Peer Reviewed Journal]

2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2017 ;ISSN: 1474-760X ;ISSN: 1474-7596 ;EISSN: 1474-760X ;DOI: 10.1186/s13059-017-1299-7 ;PMID: 28934964

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6
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
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Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

NeuroImage (Orlando, Fla.), 2014-04, Vol.90, p.449-468 [Peer Reviewed Journal]

2014 ;2015 INIST-CNRS ;Copyright © 2014. Published by Elsevier Inc. ;Copyright Elsevier Limited Apr 15, 2014 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2013.11.046 ;PMID: 24389422

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7
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
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Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

PloS one, 2017-06, Vol.12 (6), p.e0177678-e0177678 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Boughorbel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Boughorbel et al 2017 Boughorbel et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0177678 ;PMID: 28574989

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8
Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers
Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers
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Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers

European radiology, 2020-05, Vol.30 (5), p.3015-3022 [Peer Reviewed Journal]

European Society of Radiology 2019 ;European Society of Radiology 2019. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06460-w ;PMID: 32006166

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9
Class-imbalanced classifiers for high-dimensional data
Class-imbalanced classifiers for high-dimensional data
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Class-imbalanced classifiers for high-dimensional data

Briefings in bioinformatics, 2013-01, Vol.14 (1), p.13-26 [Peer Reviewed Journal]

ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbs006 ;PMID: 22408190

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10
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

PloS one, 2015-03, Vol.10 (3), p.e0118432-e0118432 [Peer Reviewed Journal]

COPYRIGHT 2015 Public Library of Science ;COPYRIGHT 2015 Public Library of Science ;2015 Saito, Rehmsmeier. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2015 Saito, Rehmsmeier 2015 Saito, Rehmsmeier ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0118432 ;PMID: 25738806

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11
Machine learning classifiers and fMRI: A tutorial overview
Machine learning classifiers and fMRI: A tutorial overview
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Machine learning classifiers and fMRI: A tutorial overview

NeuroImage (Orlando, Fla.), 2009-03, Vol.45 (1), p.S199-S209 [Peer Reviewed Journal]

2008 Elsevier Inc. ;Copyright Elsevier Limited Mar 1, 2009 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2008.11.007 ;PMID: 19070668

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12
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Sensors (Basel, Switzerland), 2021-03, Vol.21 (6), p.2222 [Peer Reviewed Journal]

2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21062222 ;PMID: 33810176

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13
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data
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Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data

Bioinformatics, 2012-01, Vol.28 (2), p.167-175 [Peer Reviewed Journal]

The Author(s) 2011. Published by Oxford University Press. 2011 ;2015 INIST-CNRS ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btr629 ;PMID: 22084253

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14
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
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Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Medical physics (Lancaster), 2018-07, Vol.45 (7), p.3449-3459 [Peer Reviewed Journal]

2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. ;ISSN: 0094-2405 ;EISSN: 2473-4209 ;DOI: 10.1002/mp.12967 ;PMID: 29763967

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15
Facial recognition from DNA using face-to-DNA classifiers
Facial recognition from DNA using face-to-DNA classifiers
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Facial recognition from DNA using face-to-DNA classifiers

Nature communications, 2019-06, Vol.10 (1), p.2557-2557, Article 2557 [Peer Reviewed Journal]

The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2019 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-019-10617-y ;PMID: 31186421

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16
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
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Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

PloS one, 2015-12, Vol.10 (12), p.e0145118-e0145118 [Peer Reviewed Journal]

COPYRIGHT 2015 Public Library of Science ;COPYRIGHT 2015 Public Library of Science ;2015 Maier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2015 Maier et al 2015 Maier et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0145118 ;PMID: 26672989

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17
Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial
Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial
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Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial

Clinical cancer research, 2021-12, Vol.27 (23), p.6559-6569 [Peer Reviewed Journal]

2021 The Authors; Published by the American Association for Cancer Research. ;2021 The Authors; Published by the American Association for Cancer Research 2021 American Association for Cancer Research ;ISSN: 1078-0432 ;EISSN: 1557-3265 ;DOI: 10.1158/1078-0432.CCR-21-1673 ;PMID: 34593530

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18
Real-World Performance of the Afirma Genomic Sequencing Classifier (GSC)—A Meta-analysis
Real-World Performance of the Afirma Genomic Sequencing Classifier (GSC)—A Meta-analysis
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Real-World Performance of the Afirma Genomic Sequencing Classifier (GSC)—A Meta-analysis

The journal of clinical endocrinology and metabolism, 2023-05, Vol.108 (6), p.1526-1532 [Peer Reviewed Journal]

The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. 2022 ;The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. ;COPYRIGHT 2023 Oxford University Press ;ISSN: 0021-972X ;EISSN: 1945-7197 ;DOI: 10.1210/clinem/dgac688 ;PMID: 36470585

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19
Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules
Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules
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Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules

The European respiratory journal, 2021-04, Vol.57 (4), p.2002485 [Peer Reviewed Journal]

Copyright ©ERS 2021. ;ISSN: 0903-1936 ;EISSN: 1399-3003 ;DOI: 10.1183/13993003.02485-2020 ;PMID: 33303552

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20
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin
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Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin

Microbiome, 2018-05, Vol.6 (1), p.90-90, Article 90 [Peer Reviewed Journal]

COPYRIGHT 2018 BioMed Central Ltd. ;The Author(s). 2018 ;ISSN: 2049-2618 ;EISSN: 2049-2618 ;DOI: 10.1186/s40168-018-0470-z ;PMID: 29773078

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