ReviewFrontiers in bioinformatics2024
A review of model evaluation metrics for machine learning in genetics and genomics.
Review in Frontiers in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in rehabilitation: a living systematic mapping review - first release.European journal of physical and rehabilitation medicine · 2025Pooled it
- A multiperspective evaluation framework of spatial transcriptomics clustering methods.NAR genomics and bioinformatics · 2026Article
- Machine Learning Integrating SERF-MEG and VEP for Diagnosis and Differential Diagnosis of Optic Neuropathies.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.BMC genomics · 2026Article
- Recent Advances in Automated Mitosis Detection in Digital Pathology: A PRISMA-Guided Systematic Review with Evaluation-Regime Stratification (2018-2025).Biomedicines · 2026Review
- Streptococcus-derived CpsA-CpsC fusion protein induces apoptosis in cervical cancer cells through in silico and in vitro methods.Scientific reports · 2026Article
- Microscopic image enhancement for accurate sickle cell disease identification using intuitionistic fuzzy driven retinex.Scientific reports · 2026Article
- Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.Molecular & cellular proteomics : MCP · 2026Article
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- Advocating the potential of artificial intelligence for syndrome discovery in syndromic surveillance systems: A scoping review.iScience · 2026Review
- Multi-Architecture Deep Learning for Early Alzheimer's Detection in MRI: Slice- and Scan-Level Analysis.International journal of environmental research and public health · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Using SHAP and LIME to Explain Machine Learning Models Predicting Comorbid Depression and Stroke From Daily Dietary Nutrient Intake in a US Population-Based Study.Food science & nutrition · 2026Article
- LSTM-based ensemble models for keystroke dynamics authentication: integrating explainable AI for transparency.Frontiers in artificial intelligence · 2026Article
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Review
- Classifying Diabetic and HealthyComputational and structural biotechnology journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has shown great promise in genetics and genomics where large and complex datasets have the potential to provide insight into many aspects of disease risk, pathogenesis of genetic disorders, and prediction of health and wellbeing. However, with this possibility there is a responsibility to exercise caution against biases and inflation of results that can have harmful unintended impacts. Therefore, researchers must understand the metrics used to evaluate ML models which can influence the critical interpretation of results. In this review we provide an overview of ML metrics for clustering, classification, and regression and highlight the advantages and disadvantages of each. We also detail common pitfalls that occur during model evaluation. Finally, we provide examples of how researchers can assess and utilise the results of ML models, specifically from a genomics perspective.
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.