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Bioinformatics Advance Access originally published online on September 22, 2005
Bioinformatics 2005 21(23):4255-4262; 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{at}oxfordjournals.org

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

Hassan M. Fathallah-Shaykh

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

Summary: 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 3D 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 3D 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 with MASH, Fs increases sensitivity significantly without lowering the high specificity of discovery. Fs facilitates studies in functional genomics and systems biology.

Contact: hfathall{at}rush.edu

Supplementary information: http://www.rushu.rush.edu/neurosci/Fathallah.html

Received on July 27, 2005; revised on September 17, 2005; accepted on September 20, 2005

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