ExpertDiscovery和UGENE集成系统,用于基因调控区域的智能分析。

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0448
Y Y Vaskin, I V Khomicheva, E V Ignatieva, E E Vityaev
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引用次数: 6

摘要

真核生物基因调控区层次结构的自动提取是生物学、数学和信息技术领域的交叉课题。解决这个问题的方法包括了解真核生物基因调控的复杂机制和应用先进的数据挖掘技术。本文讨论了集成系统实现一种强大的生物数据关系挖掘方法。该系统可以考虑到生物学家已知的基因调控区域的先验信息,在每个层次上进行分析,从一个简单的假设到一个复杂的假设寻找解决方案。将ExpertDiscovery系统集成到UGENE工具包中,为进行复杂的研究和生物学家的自动化工作提供了方便的环境。为了证明这一点,该系统已被用于识别SF1、SREBP、HNF4脊椎动物结合位点,并用于分析促进肝脏特异性转录的人类基因调控区域。
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ExpertDiscovery and UGENE integrated system for intelligent analysis of regulatory regions of genes.

The task of automatic extraction of the hierarchical structure of eukaryotic gene regulatory regions is in the junction of the fields of biology, mathematics and information technologies. A solution of the problem involves understanding of sophisticated mechanisms of eukaryotic gene regulation and applying advanced data mining technologies. In the paper the integrated system, implementing a powerful relation mining of biological data method, is discussed. The system allows taking into account prior information about the gene regulatory regions that is known by the biologist, performing the analysis on each hierarchical level, searching for a solution from a simple hypothesis to a complex one. The integration of ExpertDiscovery system into UGENE toolkit provides a convenient environment for conducting complex research and automating the work of a biologist. For demonstration, the system has been applied for recognition of SF1, SREBP, HNF4 vertebrate binding sites and for the analysis the human gene regulatory regions that promote liver-specific transcription.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
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.
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