Visualization and Analysis of a Cardio Vascular Disease- and MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches
Full article [PDF] (1,64 MB)
doi:10.2390/biecoll-jib-2010-148
September 09, 2010
October 25, 2010
November 11, 2010
PubMed ID 21068463
Björn Sommer, Evgeny S. Tiys, Benjamin Kormeier, Klaus Hippe, Sebastian J. Janowski, Timofey V. Ivanisenko, Anatoly O. Bragin, Patrizio Arrigo, Pavel S. Demenkov, Alexey V. Kochetov, Vladimir A. Ivanisenko, Nikolay A. Kolchanov and Ralf Hofestädt
Correspondence should be addressed to:
Björn Sommer
Bioinformatics/Medical Informatics Department, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
gro.somsocorcimLLEC@nullnreojb
Abstract
Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).