Machine Learning
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Computational identification of evolutionarily conserved exons
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Translation initiation site prediction on a genomic scale
Bioinformatics
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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.