title: Class prediction from time series gene expression profiles using dynamical systems kernels.
abstract: We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters. A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For pharmacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.
authors.affiliation: Institute for Computer Science, Ludwig-Maximilians-University of Munich, Oettingenstr. 67, 80538 Munich, Germany. kb@dbs.ifi.lmu.de
authors.forename: Karsten M
authors.initials: KM
authors.lastname: Borgwardt
authors.forename: S V N
authors.initials: SV
authors.lastname: Vishwanathan
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Kriegel
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D009103:Multiple Sclerosis / Q000188:drug therapy / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010597:Pharmacogenetics / Q000706:statistics & numerical data; D013997:Time Factors