Our research group focuses on developing computational approaches to analyze high-throughput drug and genetic screens, along with multi-omics datasets, to study the fundamental regulatory mechanisms underlying cancer cell response to treatments. Working closely with experimental and clinical groups, we develop integrative approaches to address one of the most pressing challenges in cancer: drug resistance.
We aim to develop the next generation of multidisciplinary engineers who can bridge the fields of computer science, cell biology, and biomedicine.
Machine Learning | Multi-omics | Functional Genomics | Drug resistance | Cancer
In this study, we introduce a new method for identifying the effects of genetic variations...
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Efficient large-scale cancer organoid technique
A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens
High-throughput technique that allows functional screening, pharmacological and functional genomics, in cancer organoids.
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Genome and metabolome
Genome and metabolome - chance and necessity
This editorial commentary piece presents and reflects on the work and vision of many great...
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MinLibCas9
Minimal genome-wide human CRISPR-Cas9 library
We developed a data-driven in silico pipeline to design and optimise a minimal genome-wide CRISPR-Cas9...
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Identifying Drug Mode-of-Action with CRISPR-Cas9
Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens
The Sanger (UK) and Broad (USA) Institutes joined efforts to generate a complete map of...
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