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Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria

机译：使用信息论标准推断遗传调控网络的连通性

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Recently, the concept of mutual information has been proposed for infering the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.
机译：最近，已经提出了互信息的概念，用于从基因表达谱中推断出遗传调控网络的结构。在分析了互信息在推断基因间相互作用中的局限性之后，本文介绍了条件互信息的概念，并在此基础上提出了两种新颖的算法来推断基因调控网络的连通性结构。所提出的一种算法具有更好的准确性，而另一种算法在简单性和灵活性方面表现出色。通过利用互信息和条件互信息，还提出了一种实用的指标来评估基因之间直接连接的可能性。该新颖的度量解决了与当前推理算法相关联的共同限制，即根据连接或断开的二分法建立了基因连通性的情况。基于合成网络生成的数据集，相对于现有的最新技术方案，可以很好地比较所提出算法的性能。所提出的算法还应用于现实的生物学测量，例如皮肤黑色素瘤数据集，并推断出有意义的生物学结果。

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