Speaker: Audrey Fu, Associate Professor of Biostatistics, School of Medicine, WSU

Time: Wednesday, February 26, from 2:30 pm to 3:30 pm

Location for in-person participants: 1146 FAB 

Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1

Title: Network inference for genomics

Abstract: Understanding how genes and biological processes regulate one another is fundamental to genomics, with broad implications for traits and diseases. However, genomic data are often complex, large-scale, and noisy, posing significant challenges for learning gene networks. In this talk, I will discuss two key problems my lab is tackling. The first problem addresses causal network inference: can we infer biological mechanisms directly from observational genomic data? We have developed several machine learning approaches that leverage the principle of Mendelian randomization to infer the structure of causal networks of genes. The second problem explores a novel approach to inferring gene regulatory networks using genomewide data. Instead of modeling these networks directly at the gene level, we view them as hierarchies, capturing global patterns at higher levels and detailed wirings among genes at lower levels. To achieve this, we frame the problem as one of hierarchical community detection and are developing a deep learning algorithm for this inference.

Short Bio: Dr. Audrey Qiuyan Fu is an Associate Professor of Biostatistics in the Department of Family Medicine and Public Health Sciences and the Center for Molecular Medicine and Genetics at the Wayne State University School of Medicine.  Main developments in her group in recent years include various statistical models and machine learning algorithms for causal network inference under the principle of Mendelian randomization for genomic data, and deep learning algorithms for analyzing high-dimensional single-cell data. Her group actively practice open science and have distributed open-source software packages in R or Python for all the methodology works in her group.  Dr. Fu's research has been supported by the NIH Centers of Biomedical Research Excellence grant, the prestigious NIH Pathway to Independence Award, and other grants from the NIH, NSF, NASA and USDA.

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