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- Original title:
- Interpretable models of gene expression in single-cell immunology
- Translated title:
- Interpretierbare Modelle beschreiben Genexpression in Einzelzellimmunologie
- Author:
- Fischer, David Sebastian
- Year:
- 2022
- Document type:
- Dissertation
- Faculty/School:
- TUM School of Life Sciences
- Advisor:
- Theis, Fabian (Prof. Dr. Dr.)
- Referee:
- Theis, Fabian (Prof. Dr. Dr.); Yosef, Nir (Prof., Ph.D.); Stegle, Oliver (Prof. Dr.)
- Language:
- en
- Subject group:
- BIO Biowissenschaften
- TUM classification:
- BIO 110; MAT 022
- Abstract:
- Single-cell omics assays provide molecular characterisations of cells. Immunology has stood out as a key application of these assays and molecular states of immune cell have been described in detail. However, mechanistic insights into diseases are often still incomplete. I addressed this limitation using interpretable machine learning models for single-cell immunology in population dynamics, antigen recognition, spatial data, sample and epigenetic variation, and automated analyses.
- Translated abstract:
- In Einzelzell-Omics wird der molekularen Zustand von Zellen gemessen. Dies Methoden haben in Immunologie detaillierte Beschreibungen von Immunzellen hervorgebracht. Trotz diese Datenfülle gibt es viele offene Fragen zu Krankheitsmechanismen. Mit Bezug auf diese Fragen habe ich interpretierbaren Modellen des maschinellen Lernens in Populationsdynamik, Antigenerkennung, räumlichen Abhängigkeiten von Zellen, epigenetischer und experimenteller Variation, und in automatisierten Analysen entwickelt.
- WWW:
- https://mediatum.ub.tum.de/?id=1634060
- Date of submission:
- 29.11.2021
- Oral examination:
- 19.07.2022
- File size:
- 4233920 bytes
- Pages:
- 58
- Urn (citeable URL):
- https://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20220719-1634060-1-1
- Last change:
- 31.05.2023
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