ArticlePloS one2013
The role of balanced training and testing data sets for binary classifiers in bioinformatics.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 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
113 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Use of Audio Signals for Detecting COVID-19: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Breath biopsy of breast cancer using sensor array signals and machine learning analysis.Scientific reports · 2021Trial
- A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer.Journal of medicinal chemistry · 2026Article
- Domain-Specific Computational, Functional and Structural Methods Enable Interpretation ofCurrent oncology (Toronto, Ont.) · 2026Article
- DualFusionNet: A fusion-based dual architecture for visual quality control on fabric surfaces.PloS one · 2026Article
- Chlamy_ChloroPred: a deep learning-based, highly accurate binary classifier for chloroplast protein prediction in the model microalga,Frontiers in microbiology · 2026Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Detecting Interspecific Positive Selection Using Convolutional Neural Networks.Molecular biology and evolution · 2025Article
- Risk factors of dementia in a cohort of individuals with mild cognitive impairment in the Italian Interceptor project.European journal of neurology · 2025Article
- Enhancing Delirium Prediction and Prevention in Elderly Patients Through Machine Learning-Based Analysis.Sultan Qaboos University medical journal · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Finding polarized communities and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum.Royal Society open science · 2025Article
- Article
- Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.JMIR formative research · 2024Article
- Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.Scientific reports · 2024Article
- Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2024Article
53 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
Abstract
Training and testing of conventional machine learning models on binary classification problems depend on the proportions of the two outcomes in the relevant data sets. This may be especially important in practical terms when real-world applications of the classifier are either highly imbalanced or occur in unknown proportions. Intuitively, it may seem sensible to train machine learning models on data similar to the target data in terms of proportions of the two binary outcomes. However, we show that this is not the case using the example of prediction of deleterious and neutral phenotypes of human missense mutations in human genome data, for which the proportion of the binary outcome is unknown. Our results indicate that using balanced training data (50% neutral and 50% deleterious) results in the highest balanced accuracy (the average of True Positive Rate and True Negative Rate), Matthews correlation coefficient, and area under ROC curves, no matter what the proportions of the two phenotypes are in the testing data. Besides balancing the data by undersampling the majority class, other techniques in machine learning include oversampling the minority class, interpolating minority-class data points and various penalties for misclassifying the minority class. However, these techniques are not commonly used in either the missense phenotype prediction problem or in the prediction of disordered residues in proteins, where the imbalance problem is substantial. The appropriate approach depends on the amount of available data and the specific problem at hand.
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.