title: Intra-relation reconstruction from inter-relation: miRNA to gene expression.
abstract: 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.
authors: Dokyoon
authors: D
authors: Kim
authors: Hyunjung
authors: H
authors: Shin
authors: Je-Gun
authors: JG
authors: Joung
authors: Su-Yeon
authors: SY
authors: Lee
authors: Ju Han
authors: JH
authors: Kim
mesh_terms: D017209:Apoptosis / Q000235:genetics; D001932:Brain Neoplasms / Q000235:genetics / Q000473:pathology; D063646:Carcinogenesis / Q000235:genetics; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D023281:Genomics; D005909:Glioblastoma / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D016019:Survival Analysis
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger