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Jun 03, 2019

Seminar - Bayesian machine learning for genomic sequencing data in biomedicine (Speaker: Dr. Yuanhua Huang)

Dr. Yuanhua Huang
EBPOD Research Fellow
European Bioinformatics Institute and University of Cambridge

Date: Monday, 3-June-2019
Time: 9:30 a.m.
Venue: Seminar Room 7, LG1/F, Laboratory Block, Faculty of Medicine Building
21 Sassoon Road, Pokfulam, Hong Kong

Bayesian methods model uncertainty of all unknown variables and combine prior belief with evidence from observations. In many biological experiments, the observations are sparse, hence it is important to learn an informative prior from additional data set to achieve an accurate estimate. The first scenario of using Bayesian methods is splicing quantification in RNA-seq data, which is still problematic for genes with low coverages or large number of isoforms. In the second scenario, Dr. Huang’s group aim to decode the clone substructures of somatic mutations and systematically characterize phenotypic and functional variations between individual clones, using single cell RNA-seq data.