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Machine Learning Approaches to Gene Recognition
April 1994 (vol. 9 no. 2)
pp. 2-10

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.

Citation:
Mark W. Craven, Jude W. Shavlik, "Machine Learning Approaches to Gene Recognition," IEEE Intelligent Systems, vol. 9, no. 2, pp. 2-10, April 1994, doi:10.1109/64.294127
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