Improving gene network inference by comparing expression time-series across species, developmental stages or tissues.
We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.
Bourque
Guillaume
G
Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, H3C 3J7, Canada. bourque@gis.a-star.edu.sg
Sankoff
David
D
D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D013045:Species Specificity; D013997:Time Factors