Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

中文翻译：

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在病毒-人蛋白质相互作用网络中基于随机游走模型鉴定与COVID-19感染相关的人类基因。

冠状病毒是在1960年代首次发现的特定冠状病毒，最近的典型冠状病毒疾病暴发的三个典型例子包括严重急性呼吸道综合症（SARS），中东呼吸综合症（MERS）和COVID-19。特别是，COVID-19当前正在引起世界范围的大流行，威胁全球人类的健康。病毒致病机制的鉴定对于进一步开发有效药物和靶向临床治疗方法很重要。病毒感染机制的延迟揭示是目前预防和治疗传染病的技术障碍之一。在这项研究中，我们提出了一种随机游动模型，以识别病毒-人蛋白相互作用网络上COVID-19的潜在病理机制，我们有效地鉴定出了一组蛋白质，这些蛋白质已被确定对COVID-19感染和类似的SARS感染具有潜在的重要性，这有助于进一步开发针对COVID-19的药物和靶向治疗方法。此外，我们构建了用于预测传染病的病理生物标志物和相关药理学目标的标准计算流程。