ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem.

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0449

P S Demenkov, T V Ivanisenko, N A Kolchanov, V A Ivanisenko

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引用次数: 40

Abstract

The ANDVisio tool is designed to reconstruct and analyze associative gene networks in the earlier developed Associative Network Discovery System (ANDSystem) software package. The ANDSystem incorporates utilities for automated extraction of knowledge from Pubmed published scientific texts, analysis of factographic databases, also the ANDCell database containing information on molecular-genetic events retrieved from texts and databases. ANDVisio is a new user's interface to the ANDCell database stored in a remote server. ANDVisio provides graphic visualization, editing, search, also saving of associative gene networks in different formats resulting from user's request. The associative gene networks describe semantic relationships between molecular-genetic objects (proteins, genes, metabolites and others), biological processes, and diseases. ANDVisio is provided with various tools to support filtering by object types, relationships between objects and information sources; graph layout; search of the shortest pathway; cycles in graphs.

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ANDVisio:一个新的工具，用于图形可视化和分析文献挖掘关联基因网络在ANDSystem。

ANDVisio工具旨在重建和分析早期开发的关联网络发现系统(ANDSystem)软件包中的关联基因网络。ANDSystem集成了从Pubmed出版的科学文本中自动提取知识的实用程序，分析因子数据库，以及包含从文本和数据库检索到的分子遗传事件信息的ANDCell数据库。ANDVisio是存储在远程服务器上的ANDCell数据库的新用户界面。ANDVisio提供图形可视化，编辑，搜索，还可以根据用户的要求保存不同格式的关联基因网络。关联基因网络描述了分子遗传对象(蛋白质、基因、代谢物等)、生物过程和疾病之间的语义关系。ANDVisio提供了各种工具来支持按对象类型、对象之间的关系和信息源进行过滤;图布局;最短路径的搜索;图中的循环。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

发文量

期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.