BACKGROUND In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene-gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships. RESULTS A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews. CONCLUSIONS The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.

中文翻译：

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利用具有内在依赖性系数的微阵列数据构建基因因果调控网络。

背景技术在过去的二十年中，生物学家已经能够通过利用高通量生物技术监测基因表达来鉴定与各种表型相关的基因标记。这些基因签名已经成功地应用于药物开发，疾病预防，作物改良等。然而，忽略基因之间的相互作用削弱了基因签名在实际应用中的预测能力。通常构建基因调控网络，以节点为代表的基因，以边缘代表基因之间的关联，通常用于分析和可视化此类基因相互作用。进一步来说，本研究试图通过使用内在依赖性系数（CID）来捕获更多的非线性和因果基因关系来测量基因与基因的关联。结果证明了使用拟南芥微阵列数据，利用CID以及固有依赖性部分系数（pCID）进行的分步程序，用于在冷胁迫下重建模拟网络和众所周知的CBF-COR途径。该程序还应用于利用水稻微阵列数据在非生物胁迫下构建bHLH基因调控途径的情况，其中推定为OsbHLH104（一种假定的植物色素相互作用因子（OsPIF14）和OsbHLH060（一种铁稳态）的正调节剂，因为最相关的基因。通过文献综述验证了推断的调控途径。结论所提出的方法可以有效地破译基因调控途径，并可能有助于在实际应用中实现更高的预测能力。文献中未提及某些调控事件可能是由于植物转录过程中调控系统的高度复杂性，鉴于生物技术的持续快速发展，这种可能性有可能在不久的将来得到证实。 。