Adapting genetic regulatory models by genetic programming.
In this paper, we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.
Eriksson
R
R
Department of Computer Science, University of Skövde, Box 408, Skövde SE-54128, Sweden. roger.eriksson@ida.his.se
Olsson
B
B
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005246:Feedback; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology*; D005809:Genes, Regulator / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D014157:Transcription Factors