Conferencistas Internacionais

Network Modeling via Time Transformation

Lurdes Inoue (University of Washington–EUA)

Time-course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such dataare thought to reflect underlying biological processes developing over time. In this talk we propose a method to examine gene network relationships using time course microarray data. We assume that a sample of gene xpression profiles is a realization of a process where each profile is modeled as a random functional transformation of a common curve. We propose measures of functional similarity and time order based on estimated time transformation functions. This allows for novel inferences on gene networks which takes full account of the timing of the functional features of the gene expression profiles. We discuss the application of our model to simulated data as well as to microarray data on prostate cancer progression.