TY - JOUR
T1 - Intra-relation reconstruction from inter-relation
T2 - miRNA to gene expression
AU - Kim, Dokyoon
AU - Shin, Hyunjung
AU - Joung, Je Gun
AU - Lee, Su Yeon
AU - Kim, Ju H.an
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631). DK’s education grant was supported by the Ministry of Health and Welfare (A112020) and by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2012M3A9D1054622). HS would like to gratefully acknowledge support from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2013R1A1A3010440/2010-0028631). In addition, we gratefully acknowledge the TCGA Consortium and all its members for the TCGA Project initiative, for providing samples, tissues, data processing and making data and results available.
Funding Information:
The publication cost for this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631). This article has been published as part of BMC Systems Biology Volume 7 Supplement 3, 2013: Twelfth International Conference on Bioinformatics (InCoB2013): Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/7/S3.
PY - 2013
Y1 - 2013
N2 - BACKGROUND: In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge.METHODS: Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the cancer clinical outcome prediction. However, the limitation of previous work was that we integrated with multi-layers of genomic data without considering of inter-relationship information between genomic features. In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study.RESULTS: In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) was adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression.CONCLUSIONS: In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.
AB - BACKGROUND: In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge.METHODS: Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the cancer clinical outcome prediction. However, the limitation of previous work was that we integrated with multi-layers of genomic data without considering of inter-relationship information between genomic features. In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study.RESULTS: In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) was adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression.CONCLUSIONS: In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.
UR - http://www.scopus.com/inward/record.url?scp=84887140935&partnerID=8YFLogxK
U2 - 10.1186/1752-0509-7-S3-S8
DO - 10.1186/1752-0509-7-S3-S8
M3 - Article
C2 - 24521265
AN - SCOPUS:84887140935
SN - 1752-0509
VL - 7
SP - S8
JO - BMC Systems Biology
JF - BMC Systems Biology
ER -