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.