1. Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning
- Author
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Oren Avram, Dmitry Shcherbakov, Yaakov Maor, Tal Pupko, Ella H. Sklan, Haim Ashkenazy, Naama Wagner, Yael Weiss-Ottolenghi, Jonathan M. Gershoni, and Smadar Hada-Neeman
- Subjects
lcsh:Immunologic diseases. Allergy ,0301 basic medicine ,Phage display ,Sample (material) ,Immunology ,DNA barcodes ,Genetic Vectors ,DNA, Recombinant ,HIV Core Protein p24 ,Oligonucleotides ,HIV Infections ,Computational biology ,Biology ,HIV Antibodies ,Communicable Diseases ,Polymerase Chain Reaction ,DNA sequencing ,Epitope ,Virus ,HIV Envelope Protein gp160 ,Antigen-Antibody Reactions ,Machine Learning ,03 medical and health sciences ,Epitopes ,0302 clinical medicine ,Peptide Library ,Immunology and Allergy ,DNA Barcoding, Taxonomic ,Humans ,Serologic Tests ,Amino Acid Sequence ,Pathogen ,Original Research ,Base Sequence ,AIDS Serodiagnosis ,High-Throughput Nucleotide Sequencing ,Hepatitis C Antibodies ,sero-diagnostics ,Hepatitis C ,Peptide Fragments ,030104 developmental biology ,Feature (computer vision) ,biology.protein ,next-generation sequencing ,phage-display ,Antibody ,Hepatitis C Antigens ,lcsh:RC581-607 ,030217 neurology & neurosurgery - 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.
- Published
- 2021