TY - JOUR
T1 - Domain-Scan
T2 - Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning
AU - Hada-Neeman, Smadar
AU - Weiss-Ottolenghi, Yael
AU - Wagner, Naama
AU - Avram, Oren
AU - Ashkenazy, Haim
AU - Maor, Yaakov
AU - Sklan, Ella H.
AU - Shcherbakov, Dmitry
AU - Pupko, Tal
AU - Gershoni, Jonathan M.
N1 - Publisher Copyright:
© Copyright © 2021 Hada-Neeman, Weiss-Ottolenghi, Wagner, Avram, Ashkenazy, Maor, Sklan, Shcherbakov, Pupko and Gershoni.
PY - 2021/2/10
Y1 - 2021/2/10
N2 - 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.
AB - 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.
KW - DNA barcodes
KW - machine learning
KW - next-generation sequencing
KW - phage-display
KW - sero-diagnostics
UR - http://www.scopus.com/inward/record.url?scp=85101559723&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2020.619896
DO - 10.3389/fimmu.2020.619896
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C2 - 33643301
AN - SCOPUS:85101559723
SN - 1664-3224
VL - 11
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 619896
ER -