ArticleFrontiers in immunology2020
Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Serum-antibody Profiling of H3N2-infected Ferrets Using a Combinatorial Phage-display Random Peptide Library.Journal of molecular biology · 2026Article
- A New Insight into Phage Combination Therapeutic Approaches Against Drug-Resistant Mixed Bacterial Infections.PHAGE (New Rochelle, N.Y.) · 2024Review
- Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data.Epidemics · 2024Review
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- Screening of sperm antigen epitopes by phage display technique and its preliminary clinical application.Basic and clinical andrology · 2022Article
- Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning.Journal of molecular biology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of pathogen-specific antibodies in an individual's blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term "Domain-Scan". We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant ("domain") is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.
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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.