ArticleFrontiers in cellular and infection microbiology2020
Machine Learning Algorithms Evaluate Immune Response to Novel
Article in Frontiers in cellular and infection microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- From gaps to progress: five-year global advances in the diagnosis of pediatric tuberculosis.Frontiers in pediatrics · 2026Review
- Quantification of Pseudomonas aeruginosa biofilms using electrochemical methods.Access microbiology · 2025Article
- Evaluation of serological assays for the diagnosis of childhood tuberculosis disease: a study protocol.BMC infectious diseases · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Integrating pathogen- and host-derived blood biomarkers for enhanced tuberculosis diagnosis: a comprehensive review.Frontiers in immunology · 2024Review
- The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm.BMC infectious diseases · 2023Article
- Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children.PLOS digital health · 2023Article
- An Immunoinformatic Strategy to Develop NewInternational journal of peptide research and therapeutics · 2022Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- HIV-Infected Patients Developing Tuberculosis Disease Show Early Changes in the Immune Response to NovelFrontiers in immunology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rationale: Tuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon- Objective: To identify optimal antigen-cytokine combinations using novel Methods: A total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Measurements and main results: We found the following four antigen-cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon- Conclusion: We exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen-cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.
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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.