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Biology 2013, 2(4), 1411-1437; doi:10.3390/biology2041411

Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, Leipzig 04107, Germany
2
LIFE, Leipzig Research Center for Civilization Diseases, Universität Leipzig, Philipp-Rosenthal-Straße 27, Leipzig 04103, Germany
*
Author to whom correspondence should be addressed.
Received: 1 August 2013 / Revised: 1 October 2013 / Accepted: 5 November 2013 / Published: 2 December 2013
(This article belongs to the Special Issue Developments in Bioinformatic Algorithms)
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Abstract

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics. View Full-Text
Keywords: co-regulated genes; molecular function; network analysis; machine learning; classifying cancer co-regulated genes; molecular function; network analysis; machine learning; classifying cancer
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hopp, L.; Lembcke, K.; Binder, H.; Wirth, H. Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes. Biology 2013, 2, 1411-1437.

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