Disease diagnostics using machine learning of B cell and T cell receptor sequences Article Swipe
Maxim Zaslavsky
,
Erin Craig
,
Jackson Michuda
,
Neha Sehgal
,
Nikhil Ram-Mohan
,
Ji-Yeun Lee
,
Khoa D. Nguyen
,
Ramona A. Hoh
,
Tho D. Pham
,
Katharina Röltgen
,
Brandon Lam
,
Ella Parsons
,
Susan Macwana
,
Wade DeJager
,
Elizabeth M. Drapeau
,
Krishna M. Roskin
,
Charlotte Cunningham‐Rundles
,
M. Anthony Moody
,
Barton F. Haynes
,
Jason D. Goldman
,
James R. Heath
,
R. Sharon Chinthrajah
,
Kari C. Nadeau
,
Benjamin A. Pinsky
,
Catherine A. Blish
,
Scott E. Hensley
,
Kent P. Jensen
,
Everett Meyer
,
Imelda Balboni
,
Paul J. Utz
,
Joan T. Merrill
,
Joel M. Guthridge
,
Judith A. James
,
Samuel Yang
,
Robert Tibshirani
,
Anshul Kundaje
,
Scott D. Boyd
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1126/science.adp2407
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1126/science.adp2407
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1126/science.adp2407
- OA Status
- green
- Cited By
- 27
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407764706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407764706Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1126/science.adp2407Digital Object Identifier
- Title
-
Disease diagnostics using machine learning of B cell and T cell receptor sequencesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-20Full publication date if available
- Authors
-
Maxim Zaslavsky, Erin Craig, Jackson Michuda, Neha Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D. Nguyen, Ramona A. Hoh, Tho D. Pham, Katharina Röltgen, Brandon Lam, Ella Parsons, Susan Macwana, Wade DeJager, Elizabeth M. Drapeau, Krishna M. Roskin, Charlotte Cunningham‐Rundles, M. Anthony Moody, Barton F. Haynes, Jason D. Goldman, James R. Heath, R. Sharon Chinthrajah, Kari C. Nadeau, Benjamin A. Pinsky, Catherine A. Blish, Scott E. Hensley, Kent P. Jensen, Everett Meyer, Imelda Balboni, Paul J. Utz, Joan T. Merrill, Joel M. Guthridge, Judith A. James, Samuel Yang, Robert Tibshirani, Anshul Kundaje, Scott D. BoydList of authors in order
- Landing page
-
https://doi.org/10.1126/science.adp2407Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/12061481Direct OA link when available
- Concepts
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Disease, Cell, Computational biology, Receptor, Computer science, Biology, Cell biology, Genetics, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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27Total citation count in OpenAlex
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2025: 27Per-year citation counts (last 5 years)
- References (count)
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97Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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