Leman Nur Nehri is a computational biologist with a PhD in Biologial Sciences, specializing in the integration of multi-omics data with probabilistic modeling to uncover regulatory mechanisms and therapeutic targets in cancer. Her expertise spans multi-omics data analysis and modeling, with a strong focus on systems-level integration, including the ability to merge large-scale datasets with small-scale, experimentally generated data to produce comprehensive insights. She combines computational approaches with experimental findings to develop biologically grounded models with high predictive power and translational potential. She has experience in both academic and industry settings, contributing to interdisciplinary projects that bridge computational biology and experimental science.
Integrating large-scale multi-omics datasets (RNA-seq, ATAC-seq, 4C-seq,
proteomics) with small-scale, wet-lab derived datasets to identify the most promising
regulatory targets for experimental validation.
Using computational modeling to select the most promising hypotheses, design more
targeted experiments, and minimize unnecessary trial-and-error in the lab.
How predictive models, guided by wet-lab data, transform even small-scale,
lab-specific datasets into meaningful and targeted predictions through a feedback
loop that accelerates discovery.