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posted 22. November 2005 22:03
Noise and rank-dependent geometrical filter improves sensitivity of highly specific discovery by microarrays
Source: Oxford Journals, Bioinformatics.
Hassan M. Fathallah-Shaykh
September 22, 2005
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
[Emphases added by ISCID News Editor]
[Link-underlined terms with ^ indicate linked entry in ISCID Encyclopedia of Science and Philosophy as added by ISCID News Editor]
Read the full pay to access research article in Oxford Journals Bioinformatics
Bioinformatics 2005 21(23):4255-4262; doi:10.1093/bioinformatics/bti684
[ 22. November 2005, 22:10: Message edited by: ISCID News Editor ]
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