One of the crucial needs in post-genomic research is to an- alyze expression matrices (e.g., SAGE and microarray data) to identify a priori interesting sets of genes, e.g., sets of genes that are frequently co-regulated. Such matrices provide expression values for given biological situations (the lines) and given genes (columns). The inductive database framework enables to support knowledge discovery processes by means of sequences of queries that concerns both data processing and pattern querying (extraction, post-processing). We provide a simple formaliza- tion of a relevant pattern domain (language of patterns, evaluation func- tions and primitive constraints) that has been proved useful for specify- ing various analysis tasks. Recent algorithmic results w.r.t. the ecient evaluation (constraint-based mining) of the so-called inductive queries are emphasized and illustrated on a 90 £ 12 636 human SAGE expres- sion matrix.
['François Rioult', 'Céline Robardet', 'Sylvain Blachon', 'Bruno Crémilleux', 'Olivier Gandrillon', 'Jean-François Boulicaut']
Mining concepts from large SAGE gene expression matrices