New statistical methods for identifying micro-ribonucleic acid (miRNA) regulatory networks

理解基因调控网络对于理解基础生物系统至关重要。这些网络对于发现与许多疾病的诊断和管理有关的基本机制具有重要意义。本研究将通过多元协方差估计和多元分类技术，为识别调控微核糖核酸模块以及了解它们在基因调控网络中的关系提供新的统计方法。我的研究结果应该使研究人员能够更好地理解生物系统的基本规律，从而改善人类健康、医学和生物学的研究成果。

Understanding gene regulatory networks is critical in the understanding of fundamental biological systems. These networks have important implications for the discovery of fundamental mechanisms relating to the diagnosis and management of many illnesses. This research will provide new statistical methods to identify regulatory micro-ribonucleic acid modules and to understand their relationship in gene regulatory networks through multiple covariance estimation and multivariate classification techniques. My results should enable researchers to better understand the regulation underlying biological systems, leading to improved human health, medical and biological research outcomes.