title: Bioinformatics tools in predictive ecology: applications to fisheries.
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.
authors.affiliation: School of Information Systems, Computing and Maths, Brunel University, Uxbridge, Middlesex UB8 3PH, UK. allan.tucker@brunel.ac.uk
authors.forename: Allan
authors.initials: A
authors.lastname: Tucker
authors.forename: Daniel
authors.initials: D
authors.lastname: Duplisea
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D004463:Ecology / Q000379:methods*; D005398:Fisheries / Q000379:methods; D005544:Forecasting / Q000379:methods*; D048389:Gadus morhua / Q000254:growth & development