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In new statistical approach, data decide model
James E.
Kloeppel, Physical Sciences Editor
217-244-1073; kloeppel@illinois.edu
Released 5/23/07
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CHAMPAIGN, Ill. —
A data-driven computational approach developed by a University of Illinois
statistician is revealing secrets about inner Earth and discovering
unique gene expressions in fruit flies, zebra fish and other living
organisms.
“Using mathematical concepts from inverse scattering and modern
statistics, we let the data ‘speak,’ and automatically generate
an appropriate model,” said Ping Ma, a professor of statistics at the U. of I. and lead author of a paper describing the technique
that has been accepted for publication in the Journal of Geophysical
Research.
To study features deep within Earth, for example, Ma and colleagues
first process the seismic data with a numeric technique called inverse
scattering. Instead of beginning with a geophysical structure and calculating
the scattering, the researchers use the scattered seismic waves to reconstruct
the scattering structures.
In that initial step, the researchers develop a generalized Radon transform
of global seismic network data to map thousands of seismograms to a
set of multiple images of the same target structure.
“These ‘common image-point gathers’ reveal common
structure among the messy seismic waves, and are the key notion that
we exploit in the statistical development of the generalized Radon transform,”
said Ma, who also is affiliated with the university’s Institute
for Genomic Biology.
In the second step, the researchers use “mixed effects”
statistical models to analyze the common image-point gathers and enhance
the generalized Radon transform images.
The combined use of the generalized Radon transform and the mixed-effect
statistical inference exploits the redundancy in the data and allows
the transformation of vast volumes of network data to statistical estimates
and quantitative analysis, Ma said.
In one recent application, Ma and colleagues at the Massachusetts Institute
of Technology and Purdue University used the numeric technique to analyze
seismic waves and infer the shape and temperature of Earth’s core-mantle
boundary region. The researchers reported their findings in the March
30, 2007, issue of the journal Science.
The data-driven statistical methodology is not limited to analyzing
seismic data. In computational biology, for example, Ma and colleagues
have used the technique to discover unique patterns of gene expression
in fruit flies and roundworms, to study differential gene expression
of the retinal development in zebra fish, and to explore the effect
of histone modifications on gene transcription rates in yeast.
The work was funded by the National Science Foundation.
Editor’s note: To reach Ping Ma, call 217-244-7095; e-mail: pingma@illinois.edu.
