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Computational Biology and Bioinformatics
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Ziv
Bar-Joseph
Dr. Bar-Joseph's primary research is in computational and systems biology.
Machine learning and statistical algorithms are used to study time series
expression experiments and to combine high throughput biological data
sources to model biological networks.
Dannie
Durand
The Durand group works in comparative genomics, focussing on the evolution
of genome organization and functional diversity in vertebrates.
Christopher
Langmead
The Langmead laboratory's interests encompass the areas of
structural and temporal phenomena in biological systems. Projects range
from new techniques for problems in structural biology to modeling of
the 3D growth of brain tumors.
Jonathan
Minden
A major objective in the Minden laboratory is developing new
tools for comparative proteomics. An important aspect of this proteomics
work is to develop automated image analysis software to detect and rank
protein changes during development and disease.
Robert
F. Murphy
Computational cell biology projects in the Murphy laboratory
include using computational approaches to analyze the patterns of proteins
in fluorescence microscope images to create self-populating knowledge
bases and models of protein subcellular location.
Gordon
S. Rule
Computational interests in the Rule laboratory include developing
methods for the rapid assignment of NMR spectral peaks and, in collaboration
with Dr. Michael Erdmann of the Computer Science Department, developing
geometric methods to detect structural homologies in proteins from NMR
data.
Russell
Schwartz
The development of modeling and simulation techniques for studying
self-assembly systems, as well as methods for analyzing and applying genome
polymorphism data are a primary area of interest in the Schwartz laboratory.
Nathan
N. Urban
In the Urban laboratory, researchers develop highly reduced and
highly detailed models of single neurons and neuronal circuits to understand
how the properties of these cells and circuits contribute to the generation
of phenomena such as oscillations, synchrony and other complex dynamical
behaviors.
Eric Xing
Eric Xing developes statistical models and machine learning algorithms for biological network inference and characterization, cis-regulatory module decoding, regulatory evolution modeling, quantitative trait locus mapping, genome polymorphism patterning, and population genetic analysis. He is also applying these quantitative approaches to investigate the mechanisms of breast cancer development and metazoan morphagenesis.
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