Work

Interpretable models of gene expression in single-cell immunology

Year: 2021

Type: dissertation

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.

Source: mediaTUM (Technical University of Munich)

Authors: Fischer, David Sebastian, Theis, Fabian, Technische Universität München

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6991986572

Cites: 29

Cited by:

Related to: 10