Wednesday | July 31, 2019
Noon – 1 p.m.
Houston Methodist Research Institute Ernest H. Cockrell Boardroom (R2-311)
Our era of big data biomedicine presents pressing demands for new analytic and modeling methods for data convergence across multiple biological scales and modalities. This presentation covers ongoing efforts to integrate rich spatial-temporal phenotypes provided by high content analysis and unbiased molecule level information from multiple-omics profiling, based on high throughput gene sequencing and proteomics technologies. In the context of cancer and neurodegeneration, such systems biologic modeling allows the identification of transcriptomic, epigenetic and proteomic biomarkers underlying the changes of disease state or biological conditions. Additionally, new biotechnologies such as image mass cytometry allows detailed spatial-temporal modeling for relationships between multi-omics markers and phenotypic changes. Ultimately, such data convergence allows phenotypic imaging data to be used as an additional layer for multi-omics modeling, offering a better understanding of disease mechanism and therapeutic strategy.
0:00 - 2:01 Introduction - Dr. Wong
2:02 - 1:09:34 - Lecture - Dr. Yin
1:09:35 - 1:25 - Q & A
Introduction by: Stephen T. Wong, PhD
John S. Dunn, Sr. Presidential Distinguished Chair in Biomedical Engineering, Full Member, Research Institute
Professor of Computer Science and Bioengineering in Oncology, Houston Methodist Academic Medicine
Associate Director, Cores, Biostatistics and Bioinformatics
Weill Cornell Medical College