A data integration framework for prediction of transcription factor targets.

A data integration framework for prediction of transcription factor targets.
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DOI:
10.1111/j.1749-6632.2008.03758.x
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发表时间:
2009-03
影响因子:
5.2
通讯作者:
Shmulevich I
Shmulevich I
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Nykter M;Lähdesmäki H;Rust A;Thorsson V;Shmulevich I

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我们提出了一个预测转录因子调控靶标的计算框架。该框架是基于使用加权和方法从DNA序列和基因表达数据中获得的多个证据来源的整合。根据训练集对证据来源进行优先排序,然后优化它们的相对贡献。在BCL6目标预测的背景下,展示了所提出的框架的性能。我们表明,当生物先验信息得到有效利用时,特别是在序列分析的情况下,该框架能够可靠地发现BCL6目标。与未合并序列信息的分数相比,该框架在性能上有相当大的提高。这一分析表明,通过评估数据的质量和生物学相关性,可以使用该计算框架获得可靠的预测。
We present a computational framework for predicting targets of transcription factor regulation. The framework is based on the integration of a number of sources of evidence, derived from DNA sequence and gene expression data, using a weighted sum approach. Sources of evidence are prioritized based on a training set, and their relative contributions are then optimized. The performance of the proposed framework is demonstrated in the context of BCL6 target prediction. We show that this framework is able to uncover BCL6 targets reliably when biological prior information is utilized effectively, particularly in the case of sequence analysis. The framework results in a considerable gain in performance over scores in which sequence information was not incorporated. This analysis shows that with assessment of the quality and biological relevance of the data, reliable predictions can be obtained with this computational framework.
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发表时间: 2005-11-29
影响因子: 11.1
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