Hayat Lab: single-cell spatial omics and target discovery
We develop computational methods that turn single-cell and spatial omics data into an understanding of human disease, and into new therapeutic targets.
Research areas
Overview of all areas- AI agents for hypothesis generation Large language model agents that generate biomedical hypotheses from single-cell, spatial omics and drug–target data.
- Spatial omics & histopathology co-learning Co-learning from spatial omics and histopathology images, with the aim of predicting molecular features from histopathology alone.
- Gene regulatory networks Inferring cell-type-specific gene-regulatory networks from single-cell data and perturbing them in silico.
- Biomedical knowledge graphs Integrating biomedical databases and single-cell data into knowledge graphs, and using graph representation learning to find therapeutic targets.
- Spatial pattern discovery Methods for the full spatial transcriptomics workflow: segmenting cells, quantifying their spatial relationships and finding recurring tissue niches.
- Virtual patient representations Transformer models that summarize the single-cell data of a patient as one representation, as a basis for in silico perturbation and for integration with electronic health records.
Publications
Content coming soon.
Open Positions
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