Next Article in Journal
Application of Fluorescence Two-Dimensional Difference In-Gel Electrophoresis as a Proteomic Biomarker Discovery Tool in Muscular Dystrophy Research
Next Article in Special Issue
Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks
Previous Article in Journal
Insights into Chromatin Structure and Dynamics in Plants
Previous Article in Special Issue
Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust
Open AccessArticle

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.
Biology 2013, 2(4), 1411-1437; https://doi.org/10.3390/biology2041411
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)
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop