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Class prediction from time series gene expression profiles using dynamical systems kernels

2006

Conference Paper

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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.

Author(s): Borgwardt, KM. and Vishwanathan, SVN. and Kriegel, H-P.
Pages: 547-558
Year: 2006
Month: January
Day: 0
Editors: Altman, R.B. A.K. Dunker, L. Hunter, T. Murray, T.E. Klein
Publisher: World Scientific

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Pacific Symposium on Biocomputing (PSB 2006)
Event Place: Maui, Hawaii

Address: Singapore
Digital: 0
ISBN: 981-256463-2

Links: PDF
Web

BibTex

@inproceedings{BorgwardtVK2006,
  title = {Class prediction from time series gene expression profiles using dynamical systems kernels},
  author = {Borgwardt, KM. and Vishwanathan, SVN. and Kriegel, H-P.},
  pages = {547-558},
  editors = {Altman, R.B.  A.K. Dunker, L. Hunter, T. Murray, T.E. Klein},
  publisher = {World Scientific},
  address = {Singapore},
  month = jan,
  year = {2006},
  month_numeric = {1}
}