Parameter settings used in CoVar evaluations.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

网络推断（Network inference）被用于对基因、蛋白质与代谢物之间的转录、信号传导及代谢相互作用进行建模，以识别影响疾病发病机制的生物通路。基于机器学习（ML）的推断模型的研究进展，展现出其可有效捕捉基因组数据中潜在模式的预测能力，这类模型正逐步成为识别驱动复杂疾病致病因素的统计模型的替代方案。本文提出CoVar这一基于机器学习的框架，其依托现有推断模型的特性，旨在发掘在不同生物学状态下驱动基因表达扰动的核心基因。与捕捉不同条件下单基因表达变化的差异表达基因（DEGs）不同，CoVar聚焦于识别那些表达网络互作图谱发生改变的变异基因，进而为解析疾病发病机制等场景下的调控动态变化提供洞见。随后，该框架可从这些变异基因的最近邻基因中筛选出核心基因——这些核心基因是变异活性的关键所在，并会影响观测到的基因表达变化背后的协同调控过程。通过对模拟数据以及由线粒体基因组缺失所扰动的酵母表达数据开展分析，本研究证实CoVar可捕捉表达数据中的内在变异性与模块化特征，并识别出现有差异分析方法无法发现的关键驱动基因。