Identifying Influenza Virus Infection Severity and Outcome Signatures Through Artificial Intelligence-driven Analyses

摘要 流感和相关疾病仍然是经济和公共卫生负担的重要来源。每一年， 大约有300万到500万人患上了严重的流感， 更多的人死于世界各地。在那些谁是不成比例的影响是个人与慢性 糖尿病、肥胖症、心血管疾病、儿童和65岁及以上的人。 例如，90%的季节性流感死亡病例和70%的流感相关住院病例属于 这个年龄组。在一周内发生心脏病发作也有很大的关联， 感染甲型或B型流感病毒。为什么有些人更容易受到影响可能是由于许多 免疫衰老、预先存在的免疫力、遗传、饮食、环境和其他潜在因素 疾病，这可能会导致流感的严重性。然而，科学家和临床医生仍然很难 确定任何给定参数或类别的疾病严重程度的轨迹。这在很大程度上是由于分配 使用单一测定或方法获得具有特定相关性的结果。考虑到众多因素， 影响疾病进展或免疫力，通过这种方法，不同的数据集被整合，以预测 结果将非常有益于指导临床实践。为了实现这一目标，我们假设， 多模态网络方法在分析不同的指标将识别功能，从复杂的数据集， 预测流感疾病的结果。这些特征包括宿主、肠道微生物和病毒因素， 以及它们之间与疾病和免疫力相关的可识别的相互作用。这将可能揭示新的 深入了解流感病毒与宿主的相互作用并改变临床实践。我们将利用现有的 免疫学测定、测序方法和元数据，以便利用现有方法 和基础设施，并将其应用于我们新颖的生物信息学和人工智能工作流程。为此，我们将 i）生成包括病毒和宿主因素的综合系统级多模式数据集，以评估 人类群组中的差异流感病毒感染严重性特征，以及ii）利用多模式网络 分析和机器学习，以确定预测疾病严重程度轨迹的特征和相互作用 因为流感病毒感染。

ABSTRACT Influenza and associated diseases remain significant sources of economic and public health burden. Every year, around three to five million people come down with severe cases of influenza with another 300,000 to 500,000 more who die worldwide. Amongst those who are disproportionately affected are individuals with chronic conditions such as diabetes, obesity, cardiovascular disease, children and individuals aged 65 years and older. For example, 90% of deaths from seasonal influenza and 70% of influenza-associated hospitalization belong to this age group. There is also a significant association between developing a heart attack within a week of getting infected with influenza A or B viruses. Why some people are more susceptible can be due to a multitude of factors such as immunosenescence, pre-existing immunity, genetics, diet, environment and other underlying diseases, which may contribute to the severity of influenza. Yet, it remains difficult for scientists and clinicians to determine the trajectory of disease severity for any given parameter or category. This is largely due to assigning an outcome with a particular correlate using a single assay or method. Given the multitude of factors that may influence disease progression or immunity, a method by which different datasets are integrated to predict the outcome would be highly beneficial in guiding clinical practices. Towards this goal, we hypothesize that a multimodal network approach in analyzing different metrics will identify features from complex datasets that are predictive of influenza disease outcome. These features include host, commensal microbial and viral factors, and identifiable interactions between them associated with disease and immunity. This will potentially reveal new insights into influenza virus-host interactions and transform clinical practices. We will utilize established immunological assays, sequencing approaches and metadata in order to take advantage of existing methods and infrastructures and apply it to our novel bioinformatics and artificial intelligence workflows. To do this, we will i) generate a comprehensive systems level multimodal dataset including both viral and host factors to assess differential influenza virus infection severity signatures in a human cohort, and ii) utilize multimodal network analysis and machine learning to identify features and interactions predictive of the trajectory of disease severity due to influenza virus infection.