Abstract
Gene recognition, gene structure prediction or gene finding, as all these three and other terms are used, consists of determining which parts of a genomic sequence are coding, and constructing the whole gene from its start site to its stop codon. Gene recognition is one of the most important open problems in Bioinformatics. The process of discovering the putative genes in a genome is called annotation.
There are two basic approaches to gene structure prediction: extrinsic and intrinsic methods. Intrinsic methods are now preferred due to their ability to identify more unknown genes. Gene recognition is a search problem, where many evidence sources are combined in a scoring function that must be maximized to obtain the structure of a probable gene.
In this paper, we propose the first purely evolutionary algorithm in the literature for gene structure prediction. The application of genetic algorithms to gene recognition will open a new field of research where the flexibility of evolutionary computation can be used to account for the complexities of the problem, which are growing as our knowledge of the molecular processes of transcription and translation deepens.
This work has been financed in part by the Excellence in Research Project P07-TIC-2682 of the Junta de Andalucía.
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Pérez-Rodríguez, J., García-Pedrajas, N. (2011). An Evolutionary Algorithm for Gene Structure Prediction. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21827-9_40
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DOI: https://doi.org/10.1007/978-3-642-21827-9_40
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