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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15263

Title: A semi-supervised tensor regression model for siRNA efficacy prediction
Authors: Thang, Bui Ngoc
Ho, Bao Tu
Kanda, Tatsuo
Keywords: RNAi
siRNA design rule
Bilinear tensor regression
Semi–supervised learning
Issue Date: 2015-03-13
Publisher: BMC Central
Magazine name: BMC Bioinformatics
Volume: 16
Start page: 80
DOI: 10.1186/s12859-015-0495-2
Abstract: Background: Short interfering RNAs (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation.Results: This work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as enriched matrices, then to employ the bilinear tensor regression to predict knockdown efficacy of those matrices. Experiments show that the proposed method achieves better results than existing models in most cases. Conclusions: Our model not only provides a suitable siRNA representation but also can predict siRNA efficacy more accurate and stable than most of state–of–the–art models. Source codes are freely available on the web at: http://www.jaist.ac.jp/~bao/BiLTR/.
Rights: Thang et al. BMC Bioinformatics (2015) 16:80, DOI : 10.1186/s12859-015-0495-2 © 2015 Thang et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
URI: http://hdl.handle.net/10119/15263
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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