July 2014

Lisa Gress, MS, joins BMI.

The BMI Department, together with the UW NIH CTSA-funded Institute for Clinical and Translational Research, is pleased to welcome Lisa Gress, MS, to the ICTR Biomedical Informatics core. Lisa will work with Biomedical Informatics core faculty--in particular Professors Mark Craven, Eneida Mendonca, and David Page--and other UW investigators on exploiting the UW electronic health record in order to advance clinical research. Lisa completed her BA degree at Kenyon College, and her MS Degree in Computational Linguistics at University of Washington, Seattle, in 2013.

Anthony Gitter joins BMI and MIR

These six medulloblastoma tumor samples all belong to the WNT subtype and exhibit functional similarities, but their mutations are quite heterogeneous. The multi-sample prize-collecting Steiner forest (Multi-PCSF) algorithm infers pathways that may be commonly perturbed by the diverse mutations. It searches a protein-protein interaction network to discover hidden, unmutated genes (circles) that connect the patient-specific mutated genes (squares) through physical interactions (figure by Tobias Ehrenberger).

The Department, together with the Morgridge Institute for Research (MIR), is very pleased to welcome Anthony Gitter, PhD, as a new Assistant Professor at UW and Investigator at MIR. Anthony completed his PhD in Computer Science at Carnegie Mellon University and a post-doctoral fellowship at Microsoft Research and MIT in Cambridge, Massachusetts. His research interests are centered in computational biology, and include probabilistic graphical models, host response to viral infection, and cancer genomics, among others. He will be joining Prof.

Associate Professor Sijian Wang has obtained new R01 methodology funding

Illustration of the performance of proposed regularized mixture Cox regression on TCGA ovarian cancer dataset. The motivation of the regularized mixture Cox regression is to address the possible heterogeneity in data.  Specifically, it automatically divide the whole population into several clusters. Simultaneously, it fits a (different) Cox model in each cluster respectively.  As shown in the figure, our regularized mixture Cox regression divides patients into three clusters: High-Risk” cluster, “Medium-

We are pleased to announce that Associate Professor Sijian Wang has obtained new R01 funding for his project entitled "Heterogeneous and Robust Survival Analysis in Genomic Studies". The four year project aims to develop, evaluate, and disseminate powerful and computationally-efficient statistical methods to model the heterogeneity in both patients and biomarkers in genomic studies, and to enhance the ability of biomarker detection and prediction power. It is funded by the National Human Genome Research Institute (NHGRI).