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Bioinformatics Vol. 18 no. 90001 2002
Pages S87-S95
© 2002 Oxford University Press

Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns

E. Myasnikova 1, A. Samsonova 2, M. Samsonova 1,* and J. Reinitz 3

1 St.Petersburg State Technical University, 29 Polytechnitcheskaya ul., St.Petersburg, 195251, Russia
2 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton-Cambridge, CB10 1SD, UK
3 University at Stony Brook, Stony Brook, NY, 11794-3600, USA

Received on January 19, 2002 ; revised on April 1, 2002 ; accepted on April 1, 2002

Motivation: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila.

Results: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm.

Availability: This software may be obtained from the authors.

Contact: samson{at}fn.csa.ru

Keywords: gene expression patterns; development; embryo staging; support vector regression; segmentation genes; Drosophila.

* To whom correspondence should be addressed.


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