AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS

Sharon Greenblum, Max Krucoff, Jacob Furst, Daniela Raicu

Abstract

A recent extension of DNA microarray technology has been its use in DNA fingerprinting. Our research involved developing an algorithm that automatically analyzes microarray images by extracting useful information while ignoring the large amounts of noise. Our data set consisted of slides generated from DNA strands of 24 different cultures of anthrax from isolated locations (all the same strain that differ only in origin-specific neutral mutations). The data set was provided by Argonne National Laboratories in Illinois. Here we present a fully automated method that classifies these isolates at least as well as the published AMIA (Automated Microarray Image Analysis) Toolbox for MATLAB with virtually no required user interaction or external information, greatly increasing efficiency of the image analysis.

References

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Paper Citation


in Harvard Style

Greenblum S., Krucoff M., Furst J. and Raicu D. (2007). AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 371-375. DOI: 10.5220/0002038603710375


in Bibtex Style

@conference{visapp07,
author={Sharon Greenblum and Max Krucoff and Jacob Furst and Daniela Raicu},
title={AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={371-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002038603710375},
isbn={978-972-8865-73-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS
SN - 978-972-8865-73-3
AU - Greenblum S.
AU - Krucoff M.
AU - Furst J.
AU - Raicu D.
PY - 2007
SP - 371
EP - 375
DO - 10.5220/0002038603710375