An Evaluation of Cloud Computing for Symptom Science Research: Moving Genomics and Machine Learning Analyses of Cancer Chemotherapy-Related Fatigue to the Cloud

癌症相关性疲劳(CRF)是癌症及其治疗中最常见的症状。 中到重度慢性肾功能衰竭对患者耐受治疗的能力以及他们的 生活质量。父母的资助解决了慢性肾功能衰竭的两个主要知识缺口：缺乏风险 预测模型以及对其基本机制缺乏了解。鉴于这些分析是数据和 由于资源密集，许多症状科学研究人员无法获得这些药物。在执行方面， 症状科学研究人员的两个主要差距是缺乏必要的计算能力 资源，以及对云部署的好处和成本缺乏了解。症状科学是一种 许多院外和院内护士科学家的突出研究重点。对解析式的评价 父赠款的管道将标识可以有效地部署到 云。考虑到云服务的潜在优势，增加对使用 用于症状科学研究的云和对成本和收益的评估可以指导未来的研究 在护理研究社区规划和更多地采用云计算。要解决这些问题 针对这些局限性，我们建议部署和评估rna-seq和机器学习的性能。 通向云的管道；开发并发布云支持的容器，用于执行定量表达 云中的甲基化(EQTM)映射；并为护理研究提供教育机会 描述我们将这些分析管道部署到云的经验的社区，并提供指导 协助规划组学和机器学习症状科学研究项目。

Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. Moderate to severe CRF has a negative impact on patients’ ability to tolerate treatments as well as on their quality of life. The parent grant addressed two of the major knowledge gaps for CRF: the lack of a risk prediction model and a lack of knowledge of its underlying mechanisms. Given these analyses are data and resource intensive, they are unavailable to many symptom science researchers. In terms of implementation, two of the major gaps for symptom science researchers are the lack of access to the necessary computational resources and a lack of understanding of benefits and costs of a cloud deployment. Symptom science is a prominent research focus for many extramural and intramural nurse scientists. An evaluation of the analytic pipelines of the parent grant would identify resource intensive analyses that could be efficiently deployed to the cloud. Given the potential benefits of cloud services, increased knowledge of the opportunities for using the cloud for symptom science research and an evaluation of the costs and benefits could guide future research planning and an increased adoption of cloud computing in the nursing research community. To address these limitations, we propose to deploy and evaluate the performance of the RNA-seq and machine learning pipelines to the cloud; develop and release a cloud-supported container for performing expression quantitative methylation (eQTM) mapping in the cloud; and provide educational opportunities for the nursing research community describing our experience deploying these analytic pipelines to the cloud and providing guidance to aid in planning omics and machine learning symptom science research projects.