Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning.
Front Immunol
; 11: 619896, 2020.
Article
en En
| MEDLINE
| ID: mdl-33643301
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Pruebas Serológicas
/
Anticuerpos Anti-VIH
/
Infecciones por VIH
/
Enfermedades Transmisibles
/
Proteína p24 del Núcleo del VIH
/
Hepatitis C
/
Antígenos de la Hepatitis C
/
Anticuerpos contra la Hepatitis C
/
Biblioteca de Péptidos
/
Proteínas gp160 de Envoltorio del VIH
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Front Immunol
Año:
2020
Tipo del documento:
Article
País de afiliación:
Israel