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Knowledge Discovery Applications in High-Throughput Polymer Characterization

Published online by Cambridge University Press:  26 February 2011

Jing Su,
Pedro Zapata and
Carson Meredith
Jing Su
Affiliation:
jsu@emory.edu, Georgia Institute of Technology, Biomedical Engineering, United States
Pedro Zapata
Affiliation:
pedro.zapata@chbe.gatech.edu, Georgia Institute of Technology, Chemical and Biomolecular Engineering, United States
Carson Meredith
Affiliation:
carson.meredith@chbe.gatech.edu, Georgia Institute of Technology, Chemical and Biomolecular Engineering, United States
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Abstract

The use of combinatorial libraries for screening cell-material interactions presents a significant need for robust informatics methods capable of extracting knowledge from large, combinatorial datasets. We describe the development of local quantitative metrics based on distributions of other cells and microstructures about each individual cell. The local metrics are shown to be superior to global, summary statistics in detecting sensitive effects of surface microstructure on proliferation of MC3T3-E1 osteoblasts.

Keywords

biomedicalpolymermorphology

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References

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