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Bioinformatics Advance Access published online on September 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti684
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 27, 2005
Revised September 17, 2005
Accepted September 20, 2005

Article

Noise and rank-dependent geometrical filter improves sensitivity of highly specific discovery by microarrays

Hassan M. Fathallah-Shaykh 1*

1 Department of Neurological Sciences, Section of Neuro-Oncology, Rush University Medical Center, 1725 West Harrison Street, Chicago, IL, 60612, USA

* To whom correspondence should be addressed.
Hassan M. Fathallah-Shaykh, E-mail: hfathall{at}rush.edu


  Abstract

MASH is a mathematical algorithm that discovers highly specific states of expression from genomic profiling by microarrays. The goal at the outset of this analysis was to improve the sensitivity of MASH. The geometrical representations of microarray datasets in the three-dimensional space are rank-dependent and unique to each dataset. The first filter (F1) of MASH defines a zone of instability whose F1-sensitive ratios have large variations. A new filter (Fs) constructs in the three-dimensional space rank-dependent lower and upper-bound contour surfaces, which are modeled based on the geometry of the unique noise intrinsic to each dataset. As compared to MASH, Fs increases sensitivity significantly without lowering the high specificity of discovery. Fs facilitates studies in functional genomics and systems biology.


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