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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

BMC genomics, 2020-01, Vol.21 (1), p.6-6, Article 6

2020

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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

BMC medical informatics and decision making, 2020-02, Vol.20 (1), p.16-16, Article 16

2020

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The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

BioData mining, 2023-02, Vol.16 (1), p.4-4, Article 4

2023

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The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

BioData mining, 2021-02, Vol.14 (1), p.13-13, Article 13

2021

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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

PeerJ. Computer science, 2021-07, Vol.7, p.e623-e623, Article e623

2021

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A comparison of MCC and CEN error measures in multi-class prediction

PloS one, 2012-08, Vol.7 (8), p.e41882

2012

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Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Scientific reports, 2020-10, Vol.10 (1), p.17156-17156, Article 17156

2020

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The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment

IEEE access, 2021, Vol.9, p.78368-78381

2021

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Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma

Nature communications, 2020-11, Vol.11 (1), p.5992-5992, Article 5992

2020

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The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment

IEEE access, 2021, Vol.9, p.47112-47124

2021

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An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

IEEE access, 2021, Vol.9, p.24485-24498

2021

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Phylogenetic convolutional neural networks in metagenomics

BMC bioinformatics, 2018-03, Vol.19 (Suppl 2), p.49-49, Article 49

2018

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An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence

Frontiers in robotics and AI, 2022-03, Vol.9, p.876814-876814

2022

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Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

PloS one, 2018-12, Vol.13 (12), p.e0208924-e0208924

2018

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Ten simple rules for providing bioinformatics support within a hospital

BioData mining, 2023-02, Vol.16 (1), p.6-12, Article 6

2023

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PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma

Clinical cancer research, 2017-08, Vol.23 (15), p.4462-4472

2017

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The ABC recommendations for validation of supervised machine learning results in biomedical sciences

Frontiers in big data, 2022-09, Vol.5, p.979465-979465

2022

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A brief survey of tools for genomic regions enrichment analysis

Frontiers in bioinformatics, 2022-10, Vol.2, p.968327-968327

2022

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Predictability of drug-induced liver injury by machine learning

Biology direct, 2020-02, Vol.15 (1), p.3-3, Article 3

2020

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Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

PLoS computational biology, 2019-03, Vol.15 (3), p.e1006269-e1006269

2019

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Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

Atmosphere, 2020-03, Vol.11 (3), p.267

2020

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Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease

IEEE access, 2021, Vol.9, p.78648-78657

2021

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GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs

Applied sciences, 2020-07, Vol.10 (14), p.4870

2020

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A null model for Pearson coexpression networks

PloS one, 2015-06, Vol.10 (6), p.e0128115-e0128115

2015

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AI models for automated segmentation of engineered polycystic kidney tubules

Scientific reports, 2024-02, Vol.14 (1), p.2847-16, Article 2847

2024

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

A comparison of MCC and CEN error measures in multi-class prediction

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment

Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma

The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment

An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

Phylogenetic convolutional neural networks in metagenomics

An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

Ten simple rules for providing bioinformatics support within a hospital

PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma

The ABC recommendations for validation of supervised machine learning results in biomedical sciences

A brief survey of tools for genomic regions enrichment analysis

Predictability of drug-induced liver injury by machine learning

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease

GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs

A null model for Pearson coexpression networks

AI models for automated segmentation of engineered polycystic kidney tubules

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