Video are now available here: http://morgridge.org/video
Video are now available here: http://morgridge.org/video
The University of Wisconsin, in a partnership with the Morgridge Institute for Research and the
Marshfield Clinic Research Foundation, has received a grant from the NIH to establish the Center
for Predictive Computational Phenotyping (CPCP), which is one of the NIH's
new Centers of Excellence for Big Data Computing in the Biomedical Sciences.
The grant will provide nearly $11 million over a four-year period.
The Center for Predictive Computational Phenotyping will develop innovative computational and statistical
methods and software for a broad range of problems that can be cast as computational phenotyping.
The term phenotype, which is derived from the Greek word phainein meaning‚ "to show"
refers to the observable properties of an organism that result from the interaction of its genotype
and its environment. Some phenotypes are easily measured and interpreted, and are available in an accessible
format. However, a wide range of scientifically and clinically important phenotypes do not satisfy these
criteria. In such cases, computational phenotyping methods are required either to extract a relevant phenotype
from a complex data source or collection of heterogeneous data sources, and to predict clinically important
phenotypes before they are exhibited. The Center will have a particular focus on screening for breast cancer
and Alzheimer's disease, and it will investigate how to exploit a wide array of data types for these tasks,
including molecular profiles, medical images, electronic health records, and population-level data. The Center
will also provide training in biomedical Big Data analysis to scientists and clinicians, and it will
investigate the bioethical issues surrounding the technology being developed.
The director of the Center is Mark Craven and the associate director is Michael Newton, both of whom are
professors in the Department of Biostatistics & Medical Informatics.
BMI Student Eric Lantz, his advisor Professor David Page, and their colleagues Matthew Fredrikson, Somesh Jha, Simon Lin, and Thomas Ristenpart have won the best paper award at USENIX Security, a major computer security conference held August 20th - 22nd, 2014, in San Diego. The paper, “Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing,” Fredrikson, Jha and Ristenpart are in Computer Sciences. Lin is at Nationwide Children's Hospital in Columbus, Ohio.
The paper demonstrates that publishing a predictive model for personalized medicine or pharmacogenetics could in some cases reveal private information about patients on whose data the model was trained, via a "model inversion attack." It also shows that while differential privacy can protect against such an attack, for current typical data set sizes if the privacy level is elevated high enough to provide meaningful privacy protection then the trained predictive model is of little or no utility.
Assistant Professor Sushmita Roy has obtained a prestigious NSF CAREER award to pursue an innovative project entitled "Comparative Network Biology to Study the Evolution of Regulatory Networks".
Central to how living cells accomplish diverse biological functions are regulatory networks that control what genes need to be activated under different environmental conditions. As evolution is the ultimate tinkerer of living systems systematic comparisons of how regulatory networks evolve to drive species-specific differences are critical to understand cellular functions. Through advances in genomics, it is now possible to measure the activity levels of almost all genes for many species. This provides a unique opportunity to systematically compare these gene activity levels across multiple organisms and link changes in activity to changes in the networks of individual species. However, this is challenging because, first, such comparisons require the regulatory networks to be known in not one but multiple species including those that are poorly characterized, and second, computational methods to compare molecular datasets across species other than DNA sequence are in their infancy. This project will address these challenges by developing novel computational methods to identify and compare regulatory networks across multiple species, and correlate regulatory network patterns of divergence to phenotypic changes.
The Departments of Biostatistics & Medical Informatics and of Population Health Sciences are pleased to welcome new Assistant Professor Yajuan Si, PhD. Yajuan is joining us after completing her doctoral work in Statistical Science at Duke University and a post-doctoral fellowship at Columbia University under the direction of Professor Andrew Gelman. Her research interests are centered around Bayesian statistics and include latent variable models, complex survey inference methods, causal inference and post-stratification, and data confidentiality protection. Welcome Yajuan!
It has historically been a challenge to perform Bayesian inference in a design-based survey context. Yet, classical analysis is not robust especially for subgroup estimation. Model-based estimates are subject to bias under misspecified model assumptions. To address these problems, Yajuan develops a nonparametric Gaussian process regression of model-based survey inference and a unified framework under multilevel regression and post-stratification in realistic settings with survey weights.
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. She brings her expertise in natural language processing to problems of exploiting free text in the EHR.
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. Paul Ahlquist's virology research group at MIR.
In a current project, large-scale cancer genomics studies have revealed the diversity of mutations across tumors, and shown that many mutations are unique to a single tumor. Anthony develops network algorithms to discover how heterogeneous mutations may disrupt the same signaling pathways to produce similar effects on cellular state.
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).
About the figure: Illustration of the performance of our 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-Risk” cluster and “Low-Risk” cluster. The left panel shows the estimated smoothed hazard function for each cluster. The middle panel shows the Kaplan-Meier curves of three clusters, which are well separated. The right panel shows the estimated regression coefficients of twelve patient-specific index scores (PSRPs) derived from gene expression data. Clearly, the estimated coefficients vary much among three clusters, indicating our hypothesized heterogeneity that different genes play roles in different clusters (patients).
Jie Liu has received the American Medical Informatics Association's Marco Ramoni Prize for the paper, New Genetic Variants Improve Personalized Breast Cancer Diagnosis. .Jie is a PhD candidate (CS) working with BMI faculty members Elizabeth Burnside and David Page, as well as with other collaborators from Marshfield Clinic, UW-Madison, and Essentia Institute of Rural Health. Peissig is a recent graduate of the ICTR-based Graduate Program in Clinical Investigation.
The Marco Ramoni Distinguished Paper Award for Translational Bioinformatics is presented annually at the AMIA Summit on Translational Bioinformatics to a first author of the paper at the meeting that best exemplifies the spirit and scholarship of Marco Ramoni in applying informatics methods to the elucidation of basic molecular biology processes that are relevant to the conquest of human disease. Recent large-scale genome-wide association studies (GWAS) have identified a number of new genetic variants associated with breast cancer. However, the degree to which these genetic variants improve breast cancer diagnosis in concert with mammography remains unknown. Using de-identified data available through the Marshfield Clinic and the Wisconsin Genomics Initiative, this paper presents the results of a case-control study of collected mammography features and 77 genetic variants which reflect the state of the art GWAS findings on breast cancer. The paper demonstrates that the incorporation of the genetic variants significantly improved breast cancer diagnosis based on mammographic findings.
The paper appears as:
New Genetic Variants Improve Personalized Breast Cancer Diagnosis. Jie Liu, David Page, Peggy Peissig, Catherine McCarty, Adedayo A. Onitilo, Amy Trentham-Dietz and Elizabeth Burnside. AMIA Summit on Translational Bioinformatics (AMIA-TBI).
Congratulations to Yaoyao Xu, Yuan Wang, Ning Leng and Yuan Li for awards garnered for their work at the current meetings of the Eastern North American Region (ENAR) of the International Biometric Society. Ning Leng won one of five poster awards for her work on EBSeq-HMM: An Empirical Bayes Hidden Markov Model for Ordered RNA-seq Experiments co-authored with Yuan Li under the direction of Professor Christina Kendziorski. Yuan Wang won a student travel award for her work on Persistence Landscape of Functional Signal and Its Application to Epileptic Electroencaphalogram Data under the direction of Associate Professor Moo Chung. Finally, Yaoyao Xu was the John Van Ryzin Award for the top student paper for her work on Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects under the direction of Associate Professor Menggang Yu and Professor of Statistics Jun Shao. The John Van Ryzin Award comes with travel money and a cash prize. These are wonderful accomplishments and we offer our heartfelt congratulations to all!