Dr. Lana Garmire’s research interest lies in developing computational methods and open-source tools for multi-omics data integration and single-cell bioinformatics. Garmire obtained her MA degree in Statistics and PhD degree in Comparative Biochemistry (Computational Biology focus) from UCBerkeley, followed by postdoctoral training in UC-San Diego. Her research has been continuously supported by National Institute of Health.
About the talk: Population genomics data generally have larger feature sizes than its sample sizes, posing challenges for deep-learning application in this field. In this talk, I will elaborate how we get around the curse of small population size, and apply deep-learning creatively to predict disease prognosis. We have developed a tool called Cox-nnet that uses gene expression data to predict patients survival via neural network. We further developed another integration tool called DeepProg, which uses multiple types of genomics data to predict patients survival via autoencoders. We demonstrate the utility of these methods on tens of thousands of cancer samples in the cancer genome atlas. Lastly, I will demonstrate the advantage of using deep-neural network models to impute gene expression, at single cell RNA-Seq level.
Join via Zoom:
https://msu.zoom.us/j/740946330 | Call in: +1 646 876 9923 | Meeting ID: 740 946 330
(Wednesday) 11:00 am - 12:00 pm
775 Woodlot Dr