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Azam Yazdani,


Azam Yazdani,

The University of Texas, School of Public Health, USA

Biography

Azam Yazdani has completed her PhD in Statistical Causal Inference from Cambridge University, England, and Friedrich Schiller Jena University, Germany. She introduced the granularity directed acyclic graph (GADG) algorithm which is recognized at the 2015 atlantic causal inference conference and won “The Thomas R. Ten Have Award”. The GDAG algorithm integrates different biological levels of granularity in large scales to identify causal networks. She has carried out first-rate studies on robust statistical structures. Her research work aims to tackle the problems of finding principals which govern the mechanism of disease to provide a better understanding of the disease mechanisms applying her expertise on data integration in large scales and generating causal relationships.

Abstract

Abstract : Identification of a human serum metabolome causal network using large scale data integration reveals pathways from the genome to risk factors and disease end points

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