Date(s) - 04/22/2015
Abstract: With the rapid applications of high throughput technologies such as next generation sequencing, the translational cancer informatics field is granted with bountiful opportunities and yet faced with big challenges at the same time. It is particularly important to address the issues of detecting multiple types of biomarkers arising from different high-throughput platforms. In this talk, I will elaborate my research projects that aim to integrate the omics profiles for cancer diagnosis and prognosis prediction. We have constructed a novel and versatile individual-oriented pathway-based modeling framework to predict patients’ survival. The pathway-level predictor performs better than the gene-based predictors, and achieves even better results when combined with clinical features. I will also elaborate our initial step towards integrating microRNA features into a pathway-based model, through developing a new machine learning based microRNA target predictor called mirMark. Lastly, I will present the progress on detecting heterogeneity among single-cell data, using the computational approach.
Speaker’s Bio: Dr. Lana Garmire is a tenure track assistant professor in translational bioinformatics, University of Hawaii Cancer Center. She obtained the MA degree in Statistics (2005) and PhD degree in Comparative Biochemistry (Computational Biology focus, 2007), both from UC-Berkeley, followed by the postdoctoral training (2008-2011) from the Bioengineering Department at UC-San Diego. Dr. Garmire’s collaborative nature has generated over 20 publications in quality journals, including Cell, Nature, PNAS and PLoS Computational Biology. Dr. Garmire is a recipient of multiple NIH grants, including NIGMS P20 and one of the first K01 awards by the NIH BD2K initiative.