Gene Structure Prediction From ManyAttributesAdam

نویسندگان

  • Adam A. Deaton
  • Rocco A. Servedio
چکیده

Considerable research eeort has been directed in recent years toward the problem of com-putationally identifying genes in DNA sequences. A fundamental component of a gene-nding system is a predictor which, when given a window of DNA sequence data, predicts whether or not it codes for protein product. In this paper we propose that mistake-driven, multiplicative-weight-update learning algorithms operating over a large feature set are well suited to to this prediction problem, and describe a system we have built which takes this approach. Our system is fast, simple, and produces more accurate classiiers than have previously been obtained for a range of diierent sequence lengths. We conclude that a system of this type will be a useful component in larger gene-nding programs.

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تاریخ انتشار 1998