Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various “omics” technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.

中文翻译：

---

确定监督和稀疏的功能基因组途径。

功能途径涉及一系列生物学改变，可能导致包括癌症在内的许多疾病的发生。随着各种“组学”技术的出现，整合来自生物层层次结构的信息以提供对该疾病的更全面理解变得可行。在许多疾病中，据信只有少数网络（每个网络规模相对较小）驱动该疾病。我们在这项研究中的目标是开发一种方法，以发现与表型相关的跨生物层的这些功能网络。我们得出了一个新颖的网络摘要矩阵（NSM），该矩阵突出了符合最小二乘回归关系的潜在途径。提出了一种通过不稳定性正则化分解NSM的称为“通过不稳定性分解网络摘要矩阵（DNSMI）”的算法。癌症基因组图谱（TCGA）程序的仿真和真实数据分析将显示出该算法的性能。