Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.
Genes (Basel). 2021 Oct 27;12(11):1713. doi: 10.3390/genes12111713.
Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.
疾病基因预测是精准医学的主要计算挑战之一。目前尚不确定疾病基因是否具有独特的功能特性，使其与其他非疾病基因区分开来，或者从网络的角度来看，它们是否随机分布在互作网络中，或者在网络拓扑中呈现出特定的模式。在这项研究中，我们提出了一种基于生物知识库（疾病-基因关联、基因功能注释等）和互作网络拓扑结构的疾病基因预测新方法。所提出的算法称为 MOSES，它基于从不同角度定义的两组略有对立的疾病特异性基因：热种子（即从数据库中获得的疾病基因）和冷种子（与互作网络中的疾病基因相距较远且不参与其生物学功能的基因）。将 MOSES 应用于 40 种疾病的数据集表明，所建议的潜在疾病基因在其参考疾病中显著富集。令人放心的是，已知和预测的疾病基因一起，往往在人类互作网络上形成一个连接的网络模块，减轻了疾病基因的分散分布，这可能是由于疾病-基因关联的缺乏和互作网络的不完整性。
