Sommer Björn, Tiys Evgeny S, Kormeier Benjamin, Hippe Klaus, Janowski Sebastian J, Ivanisenko Timofey V, Bragin Anatoly O, Arrigo Patrizio, Demenkov Pavel S, Kochetov Alexey V, Ivanisenko Vladimir A, Kolchanov Nikolay A, Hofestädt Ralf
Bio-/Medical Informatics Department, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany.
J Integr Bioinform. 2010 Nov 11;7(1):148. doi: 10.2390/biecoll-jib-2010-148.
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).
运用现代实验方法对具有社会重要性的疾病进行详细研究，已产生了大量有价值的数据。然而，对这些数据的分析和解读需要应用高效的计算技术和系统生物学方法。特别是，能够重建各种生物对象和事件关联网络的技术可能会很有用。在本出版物中，讨论了结合不同技术来创建与抽象细胞环境相关的此类网络，以便深入了解功能以及空间相互关系。结果表明，富含数据仓库内容和文本挖掘数据的实验性知识可用于从MUPP1/MPDZ（多PDZ结构域蛋白）开始重建和定位心血管疾病发展网络。
