Research
Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data
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* Corresponding author: Olivier Gandrillon Gandrillon@maccgmc.univ-lyon1.fr
1 Equipe Signalisations et identités cellulaires, Centre de Génétique Moléculaire et Cellulaire CNRS UMR 5534, Université Claude Bernard Lyon 1, 16 rue Dubois, F-69622 Villeurbanne cedex, France
2 Laboratoire d'Ingénierie des Systèmes d'Information, Institut National des Sciences Appliquées de Lyon, Bâtiment Blaise Pascal, F-69621 Villeurbanne cedex, France
Genome Biology 2002, 3:research0067-research0067.16 doi:10.1186/gb-2002-3-12-research0067
Published: 21 November 2002Additional files
Additional data file 1:
The first 566 rules (out of 1,746) generated by ac-miner on a mid-range-based boolean matrix using a 10% frequency threshold.
Format: PDF Size: 239KB Download file
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Additional data file 2:
Rules generated by ac-miner on a "5% cut-off" boolean matrix using either a 5% (A) or a 2 % (B) frequency threshold.
Format: PDF Size: 964KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional data file 3:
The longer set of rules: from the 1,746 rules generated by ac-miner on a mid-range-based boolean matrix using a 10% frequency threshold.
Format: PDF Size: 214KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional data file 4:
The SOTA clustering output translated into a color-coded Excel file.
Format: PDF Size: 197KB Download file
This file can be viewed with: Adobe Acrobat Reader