Special Seminar on June 16th at 11:00 AM given by Lana Garmire, PhD, Assistant Professor, Cancer Epidemiology Program, University of Hawaii Cancer Center
With the rapid applications of high throughput technologies such as next generation sequencing, it is particularly urgent to detecting biomarkers arising from different high-throughput platforms. In this talk, I will elaborate recent research effort in my group that aims to integrate the omics profiles for cancer diagnosis and prognosis prediction. We have constructed a versatile individual-oriented pathway-based modeling framework from multiple omics data types to predict patient prognosis and/or diagnosis. This approach performs better than those models built on the end-level measurables (genes, metabolites, SNPs etc) and they are relatively easy to transfer between data types and integrate among data types. More recently, we have employed deep-learning method for multi-omics based prognosis biomarker investigation, and demonstrated the robust predictive power in multiple liver cancer population cohorts. Lastly, I will report our most recent work on heterogeneity detection among tumor single-cell RNA-Seq data, using Small Nucleotide Variation (SNVs) as the new, alternative molecular features.