ArticleBMC genomics2020
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,383 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1,383 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.Journal of translational medicine · 2026Trial
- Deep Learning-Based Automated Computed Tomography Classification of Posterior Malleolar Fractures for a Standardized Diagnostic Framework.Journal of imaging informatics in medicine · 2026Article
- Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures.ACS omega · 2026Article
- Distinct electrophysiology and dopaminergic modulation of medium spiny neurons across divisions of the tail striatum and the dorsolateral striatum.Experimental physiology · 2026Article
- Rapid identification of feline sporotrichosis by ATR-FTIR spectroscopy coupled with machine learning.Analytical and bioanalytical chemistry · 2026Article
- Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.European heart journal. Digital health · 2026Review
- Leveraging Machine Learning to Understand the Link Between School Climate and Youth Substance Use: a Focus on Cannabis and Alcohol Use.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Machine Learning-Guided Discovery of Natural BACE1 Inhibitors with Enzymatic and Cellular Validation.ACS omega · 2026Article
- Within a Heartbeat: A Machine Learning Approach Using VCG for Heart Failure Assessment in Sinus Rhythm.Journal of personalized medicine · 2026Article
- GC-Derived Volatile Profiling of Commercial Thyme and Oregano Labeled Products and Evaluation of Portable NIR Screening.Foods (Basel, Switzerland) · 2026Article
- Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- scXpand: Pan-cancer detection of T cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing.Cell genomics · 2026Article
- Deep Learning-Based Identification of Dental Implant Systems from Two-Dimensional Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Exploratory study on blood and urine transcriptomes for identifying miRNA biomarkers in pediatric febrile bacterial and viral infections.Scientific reports · 2026Article
- Improving Fairness in Doubly Imbalanced Datasets.Scientific reports · 2026Article
- iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial intelligence for dysphagia screening: A machine learning approach.PLOS digital health · 2026Article
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
1,323 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F
resultsThe Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset.
conclusionsIn this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.