Journal Article

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

Hassan M. Fathallah-Shaykh

in Bioinformatics

Volume 21, issue 23, pages 4255-4262
ISSN: 1367-4803
Published online September 2005 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/bti684
Noise and rank-dependent geometrical filter improves sensitivity of highly specific discovery by microarrays

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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@rush.edu

Supplementary information:

Journal Article.  5429 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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