Learning rule-based models of biological process from gene expression time profiles using gene ontology.
BACKGROUND
Microarray technology enables large-scale inference of the participation of genes in biological process from similar expression profiles. Our aim is to induce classificatory models from expression data and biological knowledge that can automatically associate genes with novel hypotheses of biological process.


RESULTS
We report a systematic supervised learning approach to predicting biological process from time series of gene expression data and biological knowledge. Biological knowledge is expressed using gene ontology and this knowledge is associated with discriminatory expression-based features to form minimal decision rules. The resulting rule model is first evaluated on genes coding for proteins with known biological process roles using cross validation. Then it is used to generate hypotheses for genes for which no knowledge of participation in biological process could be found. The theoretical foundation for the methodology based on rough sets is outlined in the paper, and its practical application demonstrated on a data set previously published by Cho et al. (Nat. Genet., 27, 48-54, 2001).


BACKGROUND
The Rosetta system is available at http://www.idi.ntnu.no/~aleks/rosetta.


BACKGROUND
http://www.lcb.uu.se/~hvidsten/bioinf_cho/
Hvidsten
Torgeir R
TR
Department of Computer and Information Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway.
Laegreid
Astrid
A
Komorowski
Jan
J
D000465:Algorithms; D001185:Artificial Intelligence; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D030541:Databases, Genetic; D005347:Fibroblasts / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D009626:Terminology as Topic