Distance-based classifiers as potential diagnostic and prediction tools for human diseases

BMC Genomics

Research

Lei Wang¹  Tiange Cui¹  Boris Veytsman²  Ancha Baranova³  Sergey Bruskin⁴ 

[1] School of Systems Biology, George Mason University, David King Hall, MSN 3E1, 22030, Fairfax, VA, USA;School of Systems Biology, George Mason University, David King Hall, MSN 3E1, 22030, Fairfax, VA, USA;Computational Materials Science Center, George Mason University, Research I, MS 6A12, 22030, Fairfax, VA, USA;School of Systems Biology, George Mason University, David King Hall, MSN 3E1, 22030, Fairfax, VA, USA;Research Centre for Medical Genetics RAMS, Moskvorechye 1, 115478, Moscow, Russia;Vavilov Institute of General Genetics RAS, Gubkina str. 1, 119333, Moscow, Russia;Moscow Institute of Physics and Technology, Institutsky 9, 141700, Dolgoprudny, Russia;

关键词: Normal Sample;    Oligodendrogliomas;    Homeostatic State;    Dimensionality Curse;    Affymetrix Human Genome U133A;   

DOI  :  10.1186/1471-2164-15-S12-S10

来源: Springer

【 摘 要 】

Typically, gene expression biomarkers are being discovered in course of high-throughput experiments, for example, RNAseq or microarray profiling. Analytic pipelines that extract so-called signatures suffer from the "Dimensionality curse": the number of genes expressed exceeds the number of patients we can enroll in the study and use to train the discriminator algorithm. Hence, problems with the reproducibility of gene signatures are more common than not; when the algorithm is executed using a different training set, the resulting diagnostic signature may turn out to be completely different.In this paper we propose an alternative novel approach which takes into account quantifiable expression levels of all genes assayed. In our analysis, the cumulative gene expression pattern of an individual patient is represented as a point in the multidimensional space formed by all gene expression profiles assayed in given system, where the clusters of "normal samples" and "affected samples" and defined. The degree of separation of the given sample from the space occupied by "normal samples" reflects the drift of the sample away from homeostasis in the course of development of the pathophysiological process that underly the disease. The outlined approach was validated using the publicly available glioma dataset deposited in Rembrandt and associated with survival data. Additionally, the applicability of the distance analysis to the classification of non-malignant sampled was tested using psoriatic lesions and non-lesional matched controls as a model.Keywords: biomarkers; clustering; human diseases; RNA

【 授权许可】

Unknown   
© Veytsman et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.