[talks] M Hibbs preFPO

Melissa M Lawson mml at CS.Princeton.EDU
Fri Aug 3 13:51:26 EDT 2007

Matt Hibbs will present his preFPO on Friday August 10 at 10AM in 
Carl Icahn Labs Room 200 (CIL 200).  The members of his committee are:
Olga Troyanskaya, advisor; Kai Li and Tom Funkhouser, readers;  David
Botstein (MOL/Genomics) and Leonid Kruglyak (EEB/Genomics), 
nonreaders.  Everyone is invited to attend his talk.  His title and abstract 
follow below.

Title:  Analysis and Visualization of Large-Scale Gene Expression Microarray Compendia
Over the past decade, gene expression microarray data has become one of the most important
tools available for biologists to understand molecular processes and mechanisms on the
whole-genome scale. Microarray data provides a window into the inner workings of the
transcriptional process that is vital for cellular maintenance, development, biological
regulation, and disease progression. While an exponentially increasing amount of
microarray data is being generated for a wide variety of organisms, there is a severe lack
of methods designed to utilize the vast amount of data currently available. In my work, I
explore several techniques to meaningfully harness large-scale collections of microarray
data both to provide biologists with a greater ability to explore data repositories, and
to computationally utilize these repositories to discover novel biology.
First, I will discuss techniques for visualization-based analysis of microarray data on
the scale of individual datasets. These techniques include incorporating statistical
measures into visualization schemes and utilizing alternative views of data to gain a
broader picture. Second, I will focus on novel methods that allow users to simultaneously
view multiple datasets with the goal of providing a larger context within which to
understand individual datasets. These techniques include developing multi-dataset
visualization methods as well as utilizing new technologies such as very large format
display devices. Third, effective search and analysis techniques are required to guide
researchers and enable their effective use of large-scale repositories. I will present a
user-driven search algorithm designed to both quickly locate relevant datasets in a
collection and to then identify novel players related to the user's query. This technique
is useful as an independent search/exploration method, can be incorporated into
visualization systems, and can be used to predict novel functions for genes. I will
discuss how we have successfully used this approach to discover novel biology, including
directing a large-scale experimental investigation of S. cerevisiae mitochondrial
The combination of visualization-based analysis methods and exploratory algorithms such as
those presented are vital to future systems biology research. As data collections continue
to grow and as new forms of data are generated, it will become increasingly important to
develop methods and techniques that will allow experts to intelligently sift through the
available information to make new discoveries.

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