[Colloquium] Seminar Announcement: Data Mining for Network Discovery in Biology and Medicine - TODAY!

[Ninfa Mayorga]

Computation Institute Presentation

Speaker: KP Unnikrishnan, Ph.D., Research Assistant Professor of  
Bioinformatics, Center for Computational Medicine and Bioinformatics
Host: Ian Foster
Date: July 8, 2011
Time: 2:00 PM - 3:00 PM
Location: University of Chicago, Searle 240A, 5735 S. Ellis Avenue

Data Mining for Network Discovery in Biology and Medicine

Network analysis allows a deeper understanding of systems ranging from  
health & disease to power grids. Analysis of large time-evolving data  
sets from these systems poses unique data mining challenges. We  
present temporal data mining methods and associated statistical  
significance tests for analysis of two such systems: i) neuronal  
networks in the brain, and ii) hospital networks in the US. We uncover  
i) the functional connectivity (graphical structure) of neuronal  
networks and ii) cascades of inefficient patient transfers in hospital  
networks. We also demonstrate the use of these methods to track the  
time-evolutions of these systems and the use of these methods to build  
predictive models.

BIO:
Data Mining for Medicine: I develop data mining algorithms for  
analysis of clinical and biological data. We mine Electronic Health  
Records (EHRs) and Billing Records, along with Genomic, Proteomic, and  
Metabolomics data using these algorithms to help create "personalized  
medicine". We have established close collaborations with Computer  
Scientists, Statisticians, Mathematicians, Bioinformaticians, and  
practitioners of Clinical Medicine for this purpose. For example, we  
have started the following collaborations at the University of  
Michigan. With Prof. Theodore Iwashyna from Critical Care, we are  
analyzing the network of ICU transfers across United States. This has  
revealed cascades of inefficient transfers in the system. We plan to  
eventually propose a pilot program for the State of Michigan to create  
an efficient network of clinical transfers. With Prof. John Younger,  
we are analyzing time-stamped patient data from the Emergency  
Department to predict sepsis. This project is c urrently funded as a  
pilot by the Michigan Institute for Clinical and Health Research  
(MICHR), an NIH CTSA Center. With Prof. Charles Burant, we are  
analyzing the data (both clinical and biological) from a cohort of  
nutrition and obesity volunteers to create predictive models of  
outcome. This is currently funded as part of the NIH P30 grant to  
Michigan Nutrition and Obesity Research Center (MNORC). We are also  
working with researchers and developers at the National Center for  
Integrative Biomedical Informatics (NCIBI) to create data mining tools  
suitable for translational and clinical informatics.

Data Mining for Neuroscience: This research thrust follows a three- 
year long basic research program I led at General Motors Research. My  
collaborators in this area are Prof. P.S. Sastry (Electrical  
Engineering, Indian Institute of Science), Prof. Vijay Nair  
(Statistics, University of Michigan), and Prof. Anne Smith  
(Anesthesiology, UC Davis). We believe that by studying complex  
biological systems from a computational view point will lead to  
organizational principles that not only reveal how the natural system  
functions in its environment, but also suggest ways to engineer better  
artifacts endowed with similar intelligent behavior. In the long term,  
my research along this line will lead to better understanding of basic  
mechanisms of perception, and memory and also lead to applications in  
better neuro-prosthetic devices.

Network Analytics: Biological, other natural, and man-made systems  
form networks at many levels. Throughout my career, I have been a  
champion of interdisciplinary sciences. I have begun exploratory  
collaborations with colleagues from LS&A, Engineering, Information,  
Public Health, and Medicine at the University of Michigan in this  
regard. I plan to create, co-ordinate, and participate in large inter- 
disciplinary studies of networks, thereby helping create the field of  
Network Analytics.