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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6141

Title: Bioinformatics tools in predictive ecology: Applications to fisheries
Authors: Tucker, A
Duplisea, D
Keywords: Bioinformatics
Bayesian networks
Classification
Dynamic models
Fisheries management
Publication Date: 2012
Publisher: Royal Society Publishing
Citation: Philosophical Transactions of the Royal Society: Part B, 367: 279 - 290, Jan 2012
Abstract: There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.
Description: This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.
URI: http://rstb.royalsocietypublishing.org/content/367/1586/279.abstract
http://bura.brunel.ac.uk/handle/2438/6141
DOI: http://dx.doi.org/10.1098/rstb.2011.0184
ISSN: 0962-8436
Appears in Collections:Computer Science
Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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