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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >USING DIRECTED INFORMATION TO BUILD BIOLOGICALLY RELEVANT INFLUENCE NETWORKS
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USING DIRECTED INFORMATION TO BUILD BIOLOGICALLY RELEVANT INFLUENCE NETWORKS

机译：使用直接的信息来构建生物相关的影响网络

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摘要

The systematic inference of biologically relevant influence networks remains a challenging problem in computational biology. Even though the availability of high-throughput data has enabled the use of probabilistic models to infer the plausible structure of such networks, their true interpretation of the biology of the process is questionable. In this work, we propose a network inference methodology, based on the directed information (DTI) criterion, that incorporates the biology of transcription within the framework so as to enable experimentally verifiable inference. We use publicly available embryonic kidney and T-cell microarray datasets to demonstrate our results. We present two variants of network inference via DTI — supervised and unsupervised — and the inferred networks relevant to mammalian nephrogenesis and T-cell activation. Conformity of the obtained interactions with the literature as well as comparison with the coefficient of determination (CoD) method are demonstrated.Apart from network inference, the proposed framework enables the exploration of specific interactions, not just those revealed by data. To illustrate the latter point, a DTI-based framework to resolve interactions between transcription factor modules and target coregulated genes is proposed. Additionally, we show that DTI can be used in conjunction with mutual information to infer higher-order influence networks involving cooperative gene interactions.
机译：生物学相关影响网络的系统推断在计算生物学中仍然是一个具有挑战性的问题。尽管高通量数据的可用性使得能够使用概率模型来推断此类网络的合理结构，但它们对过程生物学的真实解释还是令人怀疑的。在这项工作中，我们提出了一种基于定向信息（DTI）标准的网络推理方法，该方法在框架内纳入了转录生物学，从而能够进行实验验证的推理。我们使用公开可用的胚胎肾脏和T细胞微阵列数据集来证明我们的结果。我们介绍了通过DTI进行网络推理的两种变体-有监督的和无监督的-以及与哺乳动物肾生成和T细胞活化有关的推断网络。证明了所获得的相互作用与文献的一致性以及与确定系数（CoD）方法的比较。除了网络推断之外，该框架还允许探索特定的相互作用，而不仅仅是数据揭示的相互作用。为了说明后一点，提出了一种基于DTI的框架来解决转录因子模块与靶基因调控基因之间的相互作用。此外，我们表明DTI可以与互信息一起使用，以推断涉及合作基因相互作用的高阶影响网络。

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