PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

CLASS PREDICTION FROM TIME SERIES GENE EXPRESSION PROFILES USING DYNAMICAL SYSTEMS KERNELS
Karsten Borgwardt, S V N Vishwanathan and Hans-Peter Kriegel
In: Pacific Symposium of Biocomputing 2006, 03 - 07 January 2006, Hawaii, USA.

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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2045
Deposited By:S V N Vishwanathan
Deposited On:16 January 2006