%0 Journal Article
%T Brain cancer prognosis: independent validation of a clinical bioinformatics approach
%A Raffaele Fronza
%A Michele Tramonti
%A William R Atchley
%A Christine Nardini
%J Journal of Clinical Bioinformatics
%D 2012
%I BioMed Central
%R 10.1186/2043-9113-2-2
%X We have recently presented in [1] an approach to identify the so called emergent properties of a biological system, i.e. properties that arise from the interaction of portions of a system. In particular, this method is based on the integration of translational (microarrays for mRNA gene expression) and post-translational (RT-PCR of miRNAs) data and applied to observations related to human brain tumors published in [2]. Emergent properties are a well known concept in Systems Theory and are now becoming more common in Systems Biology [3-6]. In general, the concept of emergent property relates to the fact that a system studied in its entirety shows features that cannot be captured when the system is observed through its (simplified) subsystems (Reductionist approach). Applied to molecular biology, this corresponds to the observation that separate analyses of different aspects of a system (e.g., transcriptional and/or post-transcriptional mechanisms) lead to results that may not be concordant with analyses of the system as a whole. This may be due to underestimating or overlooking interactions among miRNAs and mRNAs. The identification of emergent properties can be done through the use of latent variables in multivariate statistics (in particular via the use of Factor Analysis, FA, [7]). Latent variables are so-called hidden variables which are not evident in the original observed data, because they emerge from consideration of the covariance patterns when a large number of relevant variables are analyzed simultaneously.Taking advantage of the parallelism existing between biological systems' emergent properties and latent variables, we have used the ability of latent variables to describe emergent properties, by applying multivariate analysis simultaneously to different parts of a biological system, and notably to transcriptional and post-transcriptional data. In practice, each latent variable (i.e. each factor) obtained from analyzing jointly the mRNA and miRNA data co
%K glioblastoma
%K survival
%K system
%K emergent property
%K high-throughput biology
%U http://www.jclinbioinformatics.com/content/2/1/2