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From data to data-driven hypotheses

Leveraging novel multi-omics datasets to elucidate fundamental biological mechanisms underlying disease and health.

Welcome to the Beckmann Lab

Our lab members work as a team to derive data-driven hypotheses by combining data and advanced modeling techniques. A main focus is to integrate genetics and large-scale multi-omics datasets into meaningful network models that can inform on specific biological questions, both on disease and health states, to identify core nodes and subnetworks that can be informative to disease etiology and potential therapeutic targets. Our lab is also developing computational approaches to assemble a unified disease timeline across many individuals, recapitulating the progression of the disease from perturbation to outcome in sparse longitudinal datasets such as Mount Sinai COVID-19 Biobank. This assembled timeline is the basis for many downstream applications that has the unique potential to impact our understanding of the natural history of disease and the causal relationships between molecular traits. A central cog of our lab’s approach to scientific research is rigor and reproducibility, and we develop computational methods to streamline quality control, such as a post data generation sample mislabeling identification and correction framework, a tool to efficiently define randomization schemes to minimize biases in data generation, and a modeling approach that leverages technical replicates to account for technical variation on multi-omics datasets. We are excited to develop new approaches to perform big data analyses and integration of multiple layers of omics, from genome to phenome with everything in between, with the goal of better understanding health and disease.

Our lab focuses on X Y Z .