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Building Structure-Property Predictive Models Using Data Assimilation

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Computational Life Sciences II (CompLife 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4216))

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Abstract

In Chemometrics it is often the norm to develop regression methods for analysing non-linear multivariate data by using the observations (measurements) as the sole constraint. This is the case regardless of the nature of the regression method (parametric or non-parametric)[1]. In this article we present the development of a regression model using data assimilation[2] – A technique that takes into account additional available information about the “system” which the model is to represent. The new approach shows substantial improvement over the “conventional” methods[3] against which it has been compared.

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Author information

Authors and Affiliations

  1. Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, U.K

    Hamse Y. Mussa & Robert C. Glen

  2. NASA, Goddard Space Flight Centre, Greenbelt, MD, 20071, USA

    David J. Lary

Authors
  1. Hamse Y. Mussa
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  2. David J. Lary
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  3. Robert C. Glen
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Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  2. Department of Chemistry, Unilever Centre for Molecular Informatics, Cambridge University, CB2 1EW, Cambridge, UK

    Robert C. Glen

  3. ALTANA Chair for Bioinformatics and Information Mining, University of Konstanz, Germany

    Ingrid Fischer

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© 2006 Springer-Verlag Berlin Heidelberg

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Mussa, H.Y., Lary, D.J., Glen, R.C. (2006). Building Structure-Property Predictive Models Using Data Assimilation. In: R. Berthold, M., Glen, R.C., Fischer, I. (eds) Computational Life Sciences II. CompLife 2006. Lecture Notes in Computer Science(), vol 4216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875741_7

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  • DOI: https://doi.org/10.1007/11875741_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45767-1

  • Online ISBN: 978-3-540-45768-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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