ReviewHuman vaccines & immunotherapeutics2023
Predictive overfitting in immunological applications: Pitfalls and solutions.
Review in Human vaccines & immunotherapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed, 53 citations in OpenAlex.
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- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
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- Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.Food chemistry: X · 2026Review
- Novel fatty acid metabolism-related molecular subtyping and prognostic signature for breast cancer.Translational cancer research · 2026Article
- Integration of machine learning and viscoelastic testing to improve survival prediction in horses experiencing acute abdominal pain at a veterinary teaching hospital.Equine veterinary journal · 2026Article
- Noninvasive Prediction of High Ki-67 Expression in Hepatocellular Carcinoma Using Multiparametric MRI and Clinical Biomarkers.Journal of hepatocellular carcinoma · 2026Article
- Peripheral blood biomarkers in PD-1/PD-L1 immunotherapy: distinguishing predictive from prognostic biomarkers.Frontiers in immunology · 2026Review
- Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans.iScience · 2025Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Predicting the Optical Properties of Gold Nanoclusters Using Machine Learning Approach.ACS omega · 2025Article
- Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study.Virology journal · 2025Article
- Habitat-based radiomics from contrast-enhanced CT and clinical data to predict lymph node metastasis in clinical N0 peripheral lung adenocarcinoma ≤ 3 cm.Scientific reports · 2025Article
- Regression analysis for cardiothoracic surgeons: part 5-predicting risks and outcomes in surgery.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Profile and clinical significance of SPARCL1 and its prognostic significance in breast cancer.Oncology letters · 2025Article
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research.
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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.