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A hidden Markov support vector machine framework incorporating profile geometry learning for identifying microbial RNA in tiling array data

By Wen-Han Yu, Hedda Høvik and Tsute Chen

Abstract

Motivation: RNA expression signals detected by high-density genomic tiling microarrays contain comprehensive transcriptomic information of the target organism. Current methods for determining the RNA transcription units are still computation intense and lack the discriminative power. This article describes an efficient and accurate methodology to reveal complicated transcriptional architecture, including small regulatory RNAs, in microbial transcriptome profiles

Topics: Original Papers
Publisher: Oxford University Press
OAI identifier: oai:pubmedcentral.nih.gov:2913668
Provided by: PubMed Central
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