Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160163
Title: Class similarity network for coding and long non-coding RNA classification
Authors: Zhang, Yu
Long, Yahui
Kwoh, Chee Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Zhang, Y., Long, Y. & Kwoh, C. K. (2021). Class similarity network for coding and long non-coding RNA classification. BMC Bioinformatics, 22(1), 609-. https://dx.doi.org/10.1186/s12859-021-04517-6
Project: RGANS1905
2020-T1-001-130 (RG15/20)
MOE2019-T2-2-175
Journal: BMC Bioinformatics
Abstract: Long non-coding RNAs (lncRNAs) play significant roles in varieties of physiological and pathological processes.The premise of the lncRNA functional study is that the lncRNAs are identified correctly. Recently, deep learning method like convolutional neural network (CNN) has been successfully applied to identify the lncRNAs. However, the traditional CNN considers little relationships among samples via an indirect way.
URI: https://hdl.handle.net/10356/160163
ISSN: 1471-2105
DOI: 10.1186/s12859-021-04517-6
Schools: School of Computer Science and Engineering 
Rights: © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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