Relevance Vector Machines for classifying points and regions in biological sequences

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dc.creatorDown, Thomas A.
dc.creatorHubbard, Tim J. P.
dc.date2003-12-04
dc.date.accessioned2026-07-07T05:58:01Z
dc.date.available2026-07-07T05:58:01Z
dc.descriptionThe Relevance Vector Machine (RVM) is a recently developed machine learning framework capable of building simple models from large sets of candidate features. Here, we describe a protocol for using the RVM to explore very large numbers of candidate features, and a family of models which apply the power of the RVM to classifying and detecting interesting points and regions in biological sequence data. The models described here have been used successfully for predicting transcription start sites and other features in genome sequences.
dc.description16 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/q-bio/0312006
dc.identifierhttp://arxiv.org/abs/q-bio/0312006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/88208
dc.subjectGenomics
dc.titleRelevance Vector Machines for classifying points and regions in biological sequences
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