Affiliations: [a] Department of Immunology and Molecular Pathology, University College London, Gower Street, London, WC1E 6BT, UK. E-mail: P.Kellam@ucl.ac.uk | [b] Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK. E-mail: Xiaohui.Liu@brunel.ac.uk, Stephen.Swift@brunel.ac.uk, Allan.Tucker@brunel.ac.uk | [c] Department of Computer Science, Birkbeck College, Malet Street, London, WC1E 7HX, UK. E-mail: Nigel@dcs.bbk.ac.uk | [d] Department of Biochemistry and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK. E-mail: C.Orengo@biochemistry.ucl.ac.uk
Abstract: Short, high-dimensional, Multivariate Time 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 truer 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 experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a three-step 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, producing promising results.
