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Parameter Training

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Comparative Gene Finding

Part of the book series: Computational Biology ((COBO,volume 11))

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Abstract

The training of model parameters is one of the most challenging problems when constructing a gene finding algorithm. It involves finding the estimates of the parameters that optimises the performance of the model, based on a set of training sequences. In this chapter we describe some of the techniques most commonly used for this purpose in gene finding algorithms. First we go through the different features commonly included in gene finding algorithms, and discuss the different characteristics they exhibit. Next we put our focus on three main gene characteristics, namely feature length distributions, sequence compositional measures, and splice site detection models. Each section details a number of the most commonly used algorithms for the characteristic in question. In particular, the splice site section includes various Markovian models, neural networks, linear discriminant analysis, Bayesian networks and support vector machines.

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Authors and Affiliations

  1. Dept. Mathematical Sciences, Chalmers University of Technology, Eklandgatan 86, 412 96, Göteborg, Sweden

    Dr. Marina Axelson-Fisk

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  1. Dr. Marina Axelson-Fisk
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Correspondence to Marina Axelson-Fisk .

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Axelson-Fisk, M. (2010). Parameter Training. In: Comparative Gene Finding. Computational Biology, vol 11. Springer, London. https://doi.org/10.1007/978-1-84996-104-2_6

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  • DOI: https://doi.org/10.1007/978-1-84996-104-2_6

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-103-5

  • Online ISBN: 978-1-84996-104-2

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