Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80486
Title: FMSM : a novel computational model for predicting potential miRNA biomarkers for various human diseases
Authors: Sun, YW
Zhu, ZX
You, ZH
Zeng, ZJ
Huang, ZA
Huang, YA 
Keywords: Biomarker
Computational prediction
MiRNA-disease association
Expression profiles
Issue Date: 2018
Publisher: BioMed Central
Source: BMC systems biology, 31 Dec. 2018, v. 12, suppl. 9, 121, p. 1-12 How to cite?
Journal: BMC systems biology 
Abstract: Background: MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification.
Results: Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/-0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study.
Conclusions: A factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.
Description: 29th International Conference on Genome Informatics (GIW 2018): Systems Biology, Kunming, Yunnan, China, Dec 3-5, 2018
URI: http://hdl.handle.net/10397/80486
EISSN: 1752-0509
DOI: 10.1186/s12918-018-0664-9
Rights: © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
The following publication Sun, Y. W., Zhu, Z. X., You, Z. H., Zeng, Z. J., Huang, Z. A., & Huang, Y. A. (2018). FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases. BMC Systems Biology, 12(Suppl. 9), 121, 1-12 is available at https://dx.doi.org/10.1186/s12918-018-0664-9
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Sun_FMSM_Novel_Nomputational.pdf1.75 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

44
Citations as of Oct 16, 2019

Download(s)

33
Citations as of Oct 16, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.