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

> **NIH NIH R37** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $242,197

## Abstract

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.

## Key facts

- **NIH application ID:** 10827722
- **Project number:** 3R37CA233774-05S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Kord Michael Kober
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $242,197
- **Award type:** 3
- **Project period:** 2019-07-03 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10827722

## Citation

> US National Institutes of Health, RePORTER application 10827722, An Evaluation of Cloud Computing for Symptom Science Research:  Moving Genomics and Machine Learning Analyses of Cancer Chemotherapy-Related Fatigue to the Cloud (3R37CA233774-05S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10827722. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*