A framework for Modelling Short, High-Dimensional Multivariate Time Series: Preliminary Results in Virus Gene Expression Data Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2001-09-03

AUTHORS

Paul Kellam , Xiaohui Liu , Nigel Martin , Christine Orengo , Stephen Swift , Allan Tucker

ABSTRACT

Short, high-dimensionalMultivariateTime Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this more true than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a “global” view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report preliminary experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a threestep modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, which has produced promising results. More... »

PAGES

218-227

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44816-0_22

DOI

http://dx.doi.org/10.1007/3-540-44816-0_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046422653


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