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From single-cells to patient diagnosis: statistical machine learning approaches to biomedical discovery

Thursday, January 10, 2019 - 11:00 to 12:00
Kieran Campbell, Banting Postdoctoral Fellow in the Department of Statistics at UBC, Department of Molecular Oncology at BC Cancer, and at the UBC Data Science Institute
Room 4192, Earth Sciences Building (2207 Main Mall)

Recent technological advances have enabled routine collection of large quantities of biomedical information, ranging from molecular data such as gene expression in single-cells to population level datasets such as critical care databases. In this talk I will outline three current projects that apply and adapt cutting edge statistical approaches to effectively leverage this information to provide biomedical insights. Firstly, I'll introduce methodology to integrate single-cell RNA and DNA sequencing data to assign gene expression states to mutational cancer clones, discussing how we implemented our model using stochastic gradient Variational Bayes in Tensorflow. Secondly, I will introduce cellassign, a probabilistic model that automates the annotation of single-cell RNA-sequencing data to known types, allowing effective quantification of the tumour microenvironment in human cancers. Finally, I will introduce a probabilistic framework for optimizing the order of diagnostic test acquisition in a critical care context, and demonstrate how this can be applied to black box prediction functions (such as deep neural nets) in the context of differential diagnosis.