ArticlePLoS computational biology2021
Hands-on training about overfitting.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Online tutorial on survival analysis for biomarker discovery.PLoS computational biology · 2026Article
- An integrated CSF-serum biomarker model for predicting clinical progression in Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- Modern statistical techniques for cardiothoracic surgeons: Part 8-Bayesian analysis and beyond.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer.Discover oncology · 2025Article
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Applications of Machine Learning in the Diagnosis and Prognosis of Patients with Chiari Malformation Type I: A Scoping Review.Children (Basel, Switzerland) · 2025Review
- Targeting bacterial transcription factors for infection control: opportunities and challenges.Transcription · 2025Review
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Development and Application of an In Vitro Drug Screening Assay forBiomedicines · 2024Article
- Hands-on training about data clustering with orange data mining toolbox.PLoS computational biology · 2024Article
- Peripheral Blood DNA Methylation Signatures and Response to Tofacitinib in Moderate-to-severe Ulcerative Colitis.Journal of Crohn's & colitis · 2024Article
- Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation.European radiology · 2024Article
- Investigation of the myopic outcomes of the newer intraocular lens power calculation formulas in Korean patients with long eyes.Scientific reports · 2024Article
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep centroid: a general deep cascade classifier for biomedical omics data classification.Bioinformatics (Oxford, England) · 2024Article
- Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review.Journal of clinical medicine · 2024Review
- Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait.Scientific reports · 2024Article
- The benefits and pitfalls of machine learning for biomarker discovery.Cell and tissue research · 2023Review
- Article
- Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes.Biological research for nursing · 2023Review
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
Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.
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.