RAPID: Prediction of coronavirus infections and complications at the individual and the population levels from genomic, proteomic, clinical and behavioral data sources

RAPID：根据基因组、蛋白质组、临床和行为数据源预测个体和群体水平的冠状病毒感染和并发症

• 批准号: 2029543
• 负责人: Judith Klein
• 金额: $ 10万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029543&HistoricalAwards=false
• 关键词: RAPID; Prediction; coronavirus; infections; complications

As of mid-April 2020, two million people are infected worldwide with the novel coronavirus that first appeared in Wuhan, China in December of 2019. Now, the USA is at the epicenter of this pandemic, where it has already killed 20,000 people. Approaches to slow the progression are urgently needed. This requires a better fundamental understanding of the factors affecting not only virus spread, but also who develops complications and ultimately dies from the infection. It is becoming clear that many factors are at play, including molecular, physiological, lifestyle, behavioral, demographic and socio-economic ones. In particular, co-morbidities such as diabetes and high blood pressure are known risk factors for COVID-19 complications and death but are likely only the tip of the iceberg. Molecular data indicates that as many as 100 co-morbidities exist. Given this complexity, statistical approaches are needed to integrate and account for all of these factors when predicting and assessing the health risks arising from coronavirus spread and infection. This project will create computational tools that will help individuals and healthcare professionals make decisions related to coronavirus, helping target human and material resources where they are most needed. To decrease the numbers of people suffering from this pandemic, these tools are needed urgently.Integrating large numbers of risk factors through machine-learning approaches allows the building of statistical models that take all evidence into account. COVID-19 infections will be predicted at the individual and population levels. At the individual level, two binary (yes/no) classifiers will be built, (1) if an individual is likely infected with coronavirus, and if yes, (2) will the patient develop complications. As with all predictions, they cannot replace real data, but they can help prioritize who gets tested, who gets quarantined, who gets more closely monitored for signs of complications, and who gets personalized recommendations. Existing approaches include symptom-tracker apps, such as the coronavirus self-checker apps offered by the CDC, many healthcare providers and local government authorities and the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS), which determine the degree of illness of a patient. None of these approaches account for co-morbidities, and they lack the use of machine learning for data integration needed to predict individual outcomes. At the population level, possible routes of infection will be analyzed using graph analysis, through analysis of proximity, social interactions, and materials transport, taking the individual-level information into account where available. The project will be highly interdisciplinary, integrating biochemistry and computer science with ongoing input and feedback from healthcare professionals. This will ensure that the work will be relevant to the current crisis and easier to adopt by healthcare providers. Students and postdocs who participate in this research will be trained in interdisciplinary research and will be exposed directly to frontline workers in the pandemic. A publicly available, free app and a web interface will disseminate the predictions made in this project broadly in the hope it will find many users.In summary, the goal of this research is to understand how SARS-CoV-2 virus and host genomes interact to determine the full spectrum of disease outcomes, with the goals of identifying the cellular basis for host range and pathology, predicting morbidit,; and developing effective medical interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

截至 2020 年 4 月中旬，全球有 200 万人感染了 2019 年 12 月首次出现在中国武汉的新型冠状病毒。现在，美国处于这场大流行的中心，已造成 2 万人死亡。迫切需要减缓进展的方法。这需要更好地了解不仅影响病毒传播，而且影响谁出现并发症并最终死于感染的因素。越来越明显的是，许多因素都在起作用，包括分子、生理、生活方式、行为、人口和社会经济因素。特别是，糖尿病和高血压等合并症是已知的 COVID-19 并发症和死亡风险因素，但可能只是冰山一角。分子数据表明存在多达 100 种合并症。鉴于这种复杂性，在预测和评估冠状病毒传播和感染引起的健康风险时，需要采用统计方法来整合和解释所有这些因素。该项目将创建计算工具，帮助个人和医疗保健专业人员做出与冠状病毒相关的决策，帮助将人力和物力资源瞄准最需要的地方。为了减少遭受这一流行病的人数，迫切需要这些工具。通过机器学习方法整合大量风险因素，可以建立考虑所有证据的统计模型。将在个人和群体层面上预测 COVID-19 感染情况。在个体层面，将建立两个二元（是/否）分类器，（1）个体是否可能感染冠状病毒，如果是，（2）患者是否会出现并发症。与所有预测一样，它们无法取代真实数据，但可以帮助确定谁接受测试、谁被隔离、谁受到更密切的并发症迹象监测以及谁获得个性化建议的优先顺序。现有的方法包括症状跟踪应用程序，例如 CDC、许多医疗保健提供者和地方政府机构提供的冠状病毒自我检查应用程序，以及确定患者病情程度的国家早期预警评分 (NEWS) 和修正早期预警评分 (MEWS)。这些方法都没有考虑到合并症，并且它们缺乏使用机器学习来进行预测个体结果所需的数据集成。在人口层面，将通过对邻近性、社会互动和物资运输的分析，使用图形分析来分析可能的感染途径，并考虑个人层面的信息（如果有）。该项目将是高度跨学科的，将生物化学和计算机科学与医疗保健专业人员的持续投入和反馈相结合。这将确保这项工作与当前危机相关，并且更容易被医疗保健提供者采用。参与这项研究的学生和博士后将接受跨学科研究培训，并将直接接触大流行中的一线工作人员。一个公开的、免费的应用程序和一个网络界面将广泛传播该项目中所做的预测，希望它能找到更多用户。总之，这项研究的目标是了解 SARS-CoV-2 病毒和宿主基因组如何相互作用，以确定全面的疾病结果，目标是确定宿主范围和病理学的细胞基础，预测发病率；该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

使用纵向成像遗传数据学习半监督表示丰富

• DOI: 10.1109/bibm49941.2020.9313310
• 发表时间: 2020
• 期刊: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM
• 作者: Seo, Hoon;Brand, Lodewijk;Wang, Hua
• 通讯作者: Wang, Hua

On Mean-Optimal Robust Linear Discriminant Analysis

• DOI: 10.1109/icdm54844.2022.00129
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Xiangyu Li;Hua Wang

A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19

• DOI: 10.1145/3459930.3469552
• 发表时间: 2021-08
• 期刊: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
• 作者: Lodewijk Brand;L. Baker;Hua Wang

ANERGY TO SYNERGY-THE ENERGY FUELING THE RXCOVEA FRAMEWORK

• DOI: 10.1615/intjmultcompeng.2020035097
• 发表时间: 2020-01-01
• 期刊: INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING
• 影响因子: 1.4
• 作者: Bischof, Evelyne;Broek, Jantine A. C.;Schlick, Tamar
• 通讯作者: Schlick, Tamar

Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

整合静态和动态数据，使用增强的基因型-表型表示改进认知衰退的预测

• 发表时间: 2021
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Seo, Hoon;Brand, Lodewijk;Wang, Hua;Nie, Feiping
• 通讯作者: Nie, Feiping