ArticleFrontiers in medicine2021
An Introduction to Machine Learning Approaches for Biomedical Research.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrating Machine Learning into Myositis Research: a Systematic Review.Clinical reviews in allergy & immunology · 2025Pooled it
- Artificial Intelligence for Predicting Perioperative Outcomes in Cardiac Surgery: A Systematic Review.Innovations (Philadelphia, Pa.)Pooled it
- Ensemble machine learning classifiers based on computed tomography radiomics for predicting spread through air spaces in lung adenocarcinoma: a multicenter retrospective cohort study with transcriptomic interpretation.Quantitative imaging in medicine and surgery · 2026Article
- Machine learning-enabled diagnosis of viral respiratory infections from exhaled volatile organic compound analysis.BMC pulmonary medicine · 2026Article
- Genotype-phenotype modeling of light ecotypes inbioRxiv : the preprint server for biology · 2026Article
- Baseline pain, fatigue, and sleep quality predict 12-week pain improvement in inflammatory arthritis: retrospective real-world analysis of a digital health application cohort.Rheumatology international · 2026Article
- Structural signature of plasma proteins classifies the status of Alzheimer's disease.Nature aging · 2026Article
- Frequency Ranking of Imaging Biomarkers for Lung Cancer Risk Stratification Using a Hybrid Elastic Net Method.Cancers · 2026Article
- Innovative statistical method for longitudinal and hierarchical data modeling: the GMEXGBoost method.BMC medical research methodology · 2026Article
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Mapping motor cortex "hand knob" metrics to symptom hemidominance in Parkinson's disease: from width comparison to machine learning.Brain & spine · 2026Article
- Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.Frontiers in bioinformatics · 2026Article
- Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making.BMC medical research methodology · 2025Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review.Sensors (Basel, Switzerland) · 2025Review
- Explorative Detection of Fractional Exhaled Nitric Oxide (FeNO) in Exhaled Breath of Patients With Breast Cancer.Cancer medicine · 2025Article
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of animals, it predicts weather an animal is treated or control, or whether molecules have the potential to interact or not, etc. ML approaches can also find such patterns in an agnostic manner, i.e., without having information about the classes. Respectively, those methods are referred to as supervised and unsupervised ML. A third type of ML is reinforcement learning, which attempts to find a sequence of actions that contribute to achieving a specific goal. All of these methods are becoming increasingly popular in biomedical research in quite diverse areas including drug design, stratification of patients, medical images analysis, molecular interactions, prediction of therapy outcomes and many more. We describe several supervised and unsupervised ML techniques, and illustrate a series of prototypical examples using state-of-the-art computational approaches. Given the complexity of reinforcement learning, it is not discussed in detail here, instead, interested readers are referred to excellent reviews on that topic. We focus on concepts rather than procedures, as our goal is to attract the attention of researchers in biomedicine toward the plethora of powerful ML methods and their potential to leverage basic and applied research programs.
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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.