Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602; Institute of Plant Breeding, Genetics and Genomics, University of Georgia, 111 Riverbend Road, Athens, GA 30602; Department of Statistics, University of Georgia, 101 Cedar Street, Athens, GA 30602.
Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602.
Trends Plant Sci. 2015 Oct;20(10):664-675. doi: 10.1016/j.tplants.2015.06.013.
Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants.
尽管在植物中已经获得了大量全基因组基因表达数据，但有效地挖掘这些信息以发现基因和基因网络，例如控制农艺重要性状的基因网络，仍然是一项挑战。这些网络反映了基因之间的潜在相互作用，因此可以系统地理解目标生物过程的分子机制。我们讨论了使用基因表达数据分析基因网络的方法，特别是重点介绍了用于重建网络的四种常见统计方法：相关性、有监督学习中的特征选择、概率图形模型和元预测。此外，我们还讨论了有效利用这些方法深入了解植物中的生物系统。
