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Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks

BACKGROUND: Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genom...

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Autores principales: Guo, Yang, Qi, Yang, Li, Zhanhuai, Shang, Xuequn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311915/
https://www.ncbi.nlm.nih.gov/pubmed/30598111
http://dx.doi.org/10.1186/s12920-018-0435-x
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author Guo, Yang
Qi, Yang
Li, Zhanhuai
Shang, Xuequn
author_facet Guo, Yang
Qi, Yang
Li, Zhanhuai
Shang, Xuequn
author_sort Guo, Yang
collection PubMed
description BACKGROUND: Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genome features and the contribution weights of different data-views in data integration. It is widely accepted that the regulatory associations between features play important roles in cancer subtype studies. In addition, different data-views may have different contributions in data integration for cancer subtype prediction. RESULTS: In this paper, we propose a method, CSPRV, to improve the cancer subtype prediction by incorporating multi-sources transcriptome expression data and heterogeneous biological networks. We extract multiple expression features of each genome element based on the regulatory associations in the heterogeneous biological networks and use a generalized matrix correlation method (RV(2)) to predict the similarities between samples in each view of expression data. We fuse the similarity information in multiple data-views according to different integration weights. Based on the integrated similarities between samples, we cluster samples into different subtype groups. Comprehensive experiments on TCGA cancer datasets demonstrate that the proposed method can identify more clinically meaningful cancer subtypes comparing with most existing methods. CONCLUSIONS: The consideration of regulatory associations between biological features and data-views contribution is important to improve the understanding of cancer subtypes. The proposed method provides an open framework to incorporate transcriptome expression data and biological regulation network to predict cancer subtypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0435-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-63119152019-01-07 Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks Guo, Yang Qi, Yang Li, Zhanhuai Shang, Xuequn BMC Med Genomics Research BACKGROUND: Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genome features and the contribution weights of different data-views in data integration. It is widely accepted that the regulatory associations between features play important roles in cancer subtype studies. In addition, different data-views may have different contributions in data integration for cancer subtype prediction. RESULTS: In this paper, we propose a method, CSPRV, to improve the cancer subtype prediction by incorporating multi-sources transcriptome expression data and heterogeneous biological networks. We extract multiple expression features of each genome element based on the regulatory associations in the heterogeneous biological networks and use a generalized matrix correlation method (RV(2)) to predict the similarities between samples in each view of expression data. We fuse the similarity information in multiple data-views according to different integration weights. Based on the integrated similarities between samples, we cluster samples into different subtype groups. Comprehensive experiments on TCGA cancer datasets demonstrate that the proposed method can identify more clinically meaningful cancer subtypes comparing with most existing methods. CONCLUSIONS: The consideration of regulatory associations between biological features and data-views contribution is important to improve the understanding of cancer subtypes. The proposed method provides an open framework to incorporate transcriptome expression data and biological regulation network to predict cancer subtypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0435-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-31 /pmc/articles/PMC6311915/ /pubmed/30598111 http://dx.doi.org/10.1186/s12920-018-0435-x Text en © 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.
spellingShingle Research
Guo, Yang
Qi, Yang
Li, Zhanhuai
Shang, Xuequn
Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title_full Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title_fullStr Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title_full_unstemmed Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title_short Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
title_sort improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311915/
https://www.ncbi.nlm.nih.gov/pubmed/30598111
http://dx.doi.org/10.1186/s12920-018-0435-x
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