Making sense of silence in gene regulatory networks

MBZUAI · Significant research

Research Healthcare NLP Partnership Ethics

Summary

MBZUAI researchers collaborated with Carnegie Mellon University and the Broad Institute of MIT and Harvard to develop a new statistical method for analyzing data used for gene regulatory network inference. The method addresses the challenge of distinguishing true zero expression values from dropouts in single-cell RNA sequencing data. This research will be presented at the Twelfth International Conference on Learning Representations (ICLR 2024). Why it matters: Improving gene regulatory network inference can lead to better understanding of disease mechanisms and inform the development of new medicines.

Keywords

MBZUAI · gene regulatory networks · ICLR 2024 · RNA sequencing · gene expression

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