Machine Learning
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Efficient Classification of Massive, Unsegmented Datastreams
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
IEEE Intelligent Systems
Mining biomolecular data using background knowledge and artificial neural networks
Handbook of massive data sets
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
New voting strategies designed for the classification of nucleic sequences
Knowledge and Information Systems
Human splice site identification with multiclass support vector machines and bagging
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Machine learning techniques for predicting bacillus subtilis promoters
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
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As laboratories round the world produce ever-greater volumes of DNA sequence data, efficient computational analysis techniques are becoming essential. This article surveys several efforts that apply machine learning techniques to gene recognition. Machine learning methods are well suited to sequence analysis because they can learn useful descriptions of genetic concepts when given only instances, rather than explicit definitions, of those concepts. This article looks at several such approaches to gene recognition in two broad classes: search by signal and search by content.