The Cancer Informatics Shared Resource (CISR) at the University of Florida Health Cancer Institute provides centralized expertise and infrastructure in biomedical, translational and population cancer informatics to enhance scientific collaboration and accelerate research productivity across the University of Florida. The CISR is equipped to provide expertise and collaboration for researchers across UF, with a priority on cancer-focused studies.
Cancer informatics shared resource (CISR)
Mission
The advancement of cancer prevention, diagnostics and treatments depends on clinical research and quality improvement efforts, as well as translational and broader population research. The CISR supports researchers by enabling the integration and analysis of complex, heterogeneous data sources, including electronic health records (EHRs), tumor registries, clinical trials, molecular and genomic data, and patient-reported outcomes (PROs), and by delivering services in clinical data integration, artificial intelligence and machine learning (AI/ML), and digital health implementation.
Key Focus Areas & Goals
The CISR focuses on the following key areas:
- Cancer Research Data Commons
- Cancer Informatics Tools and Methods
- Cancer Surveillance
- eHealth and mHealth: Digital Health
- Natural Language Processing (NLP)
Units
The CISR is organized into three integrated units:
- Clinical Data Integration & Discovery
- Machine Learning (ML) & Generative AI
- eHealth & Patient-Centered Informatics
Together, these units provide end-to-end informatics support spanning study design, data curation, advanced analytics and technology-enabled research tools.
Cancer informatics shared resource (CISR)
Services
The CISR provides an extensive suite of informatics services, including: (1) end-to-end data curation, integration and visualization, (2) development and deployment of AI/ML algorithms, including NLP/large language model (LLM) applications, and (3) development and implementation of electronic health (eHealth) tools. The CISR supports consultations, feasibility assessments, scientific publications and grant applications. The CISR establishes best practices and standard operating procedures to ensure quality and reproducibility.
Expertise
The CISR provides expertise in:
Data Curation, Integration, & Visualization:
A core function of the CISR is to help researchers navigate complex internal and external data resources and support the collection, management, integration and visualization of research data. The CISR enables access to unique Real-world Data (RWD) assets within UF, particularly EHR data from UF Health and the OneFlorida+ clinical research network (CRN), the largest clinical data repository in Florida. The CISR also facilitates access to external, public and commercial cancer-relevant datasets such as SEER and SEER-Medicare. In addition to enabling access, it provides data management and coordination services, with a focus on RWD-based clinical and population science studies.
As part of its role in developing and maintaining the Cancer Enterprise Data Warehouse (EDW) and UF Cancer Research Data Commons (CRDC), the CISR curates, integrates and harmonizes diverse data sources to provide standardized and secure access for investigators. For EHR data from UF Health, the CISR also serves as an honest broker, ensuring appropriate governance and protection of PHI under its assisted-access model.
Development & Deployment of AI/ML Algorithms:
The CISR supports the development, deployment and operationalization of AI/ML algorithms, including a variety of supervised and unsupervised learning approaches applied to diverse data types. The CISR also specializes in NLP/LLM applications that extract clinically meaningful concepts from unstructured EHR data (e.g., pathology reports), which enhances data completeness and enables downstream modeling.
This support includes: (1) data preprocessing and feature engineering; (2) model development, training, and validation; (3) performance evaluation using best-practice metrics and external benchmarks; and (4) integration of models into research workflows and translational pipelines. Dr. Wu, co-lead of the Machine Learning (ML) and Generative AI Unit, is a nationally recognized leader in clinical NLP and AI. He also serves as the director of NLP for OneFlorida+ CRN and chief data scientist for the UF Clinical and Translational Sciences Institute (CTSI).
Development & Implementation of eHealth Tools:
The CISR supports the development of eHealth applications, including both web-based and mobile health tools such as patient portals, symptom trackers, self-management applications and clinical decision support systems.
This support spans the full lifecycle of these tools, including: (1) application development and deployment; (2) associated data collection, processing, and management; and (3) integration with clinical systems such as EHRs, in close collaboration with UF Health Research IT. Dr. Gregory, who leads the CISR’s eHealth & Patient-Centered Informatics Unit and serves as the faculty liaison for UF Health Research IT, contributes actively to these efforts.
Submit a Request Form
The CISR supports UF researchers across the full lifecycle of cancer research, from study design and development through deployment and evaluation.
To get started and help us route your request quickly and provide the right level of support, please complete the form that matches where you are in your project timeline.
Cancer informatics shared resource (CISR)
Pre-Award Request Form
Use the Pre-Award Request Form if you are developing a project or preparing a grant proposal and do not yet have funding. This includes: (1) Designing a new project or pilot project, (2) Preparing a grant application (NIH, foundation, internal, etc.), (3) Needing feasibility or technical guidance before submission , (4) Requesting a letter of support, (5) Building your methods or informatics plan, (6) Developing a budget for CISR-related activities, & (7) Seeking Co-Investigator or collaborator-level support on a proposal. At this stage, we provide consultation and proposal development support to strengthen your submission and ensure feasibility.
Cancer informatics shared resource (CISR)
Post-Award Request Form
Use the Post-Award Request Form if you already have funding or are ready to begin your project. This includes:Â (1) Funded grants or contracts, (2) Internal pilot awards, (3) Industry or foundation projects, (4) Departmentally supported or unfunded research that needs active build or implementation, & (5) Projects ready for:Â PRO or survey deployment, usability testing, wearables or device integration, workflow analysis, data collection or management, & ongoing analytic or operational support. At this stage, the we provide hands-on project support to build, deploy, and maintain your tools and data infrastructure.Â
Not sure which form to use?
If you’re unsure, a simple rule of thumb:
- Still writing or planning → Pre-Award
- Already funded or ready to begin → Post-Award
You are always welcome to contact us directly for guidance.
Compensation Requirements
The CISR provides both free and cost-associated support, depending on project needs.
Free Support
Clinical Data Integration & Discovery Unit:
- Coming Soon in 2026
Machine Learning (ML) & Generative AI Unit:
- Coming Soon in 2026
eHealth & Patient-Centered Informatics Unit:
- Study design consultation (quantitative, qualitative, mixed methods)
- eHealth tool and platform recommendations
- Feasibility assessment
- Budget planning
- Letters of support
- Grant proposal sections
- Manuscript contributions (co-authorship model)
Cost-Associated Support (Hourly Rate, Flat Fee, or FTE-based)
Clinical Data Integration & Discovery Unit:
- Coming Soon in 2026
Machine Learning (ML) & Generative AI Unit:
- Coming Soon in 2026
eHealth & Patient-Centered Informatics Unit:
- Patient-reported data collection
- App builds
- Prototyping and co-design
- Usability testing
- Workflow mapping
- Analysis of survey/interview/observational data
- Mobile app development consultation
- Virtual education or training tools
FAQs
Below are answers to frequently asked questions about the CISR.
Types of Cancer Projects Supported
Clinical Data Integration & Discovery Unit:
- Coming Soon in 2026
Machine Learning (ML) & Generative AI Unit:
- Coming Soon in 2026
eHealth & Patient-Centered Informatics Unit:
- Symptom monitoring and PROs
- Behavioral interventions delivered through eHealth
- mHealth cancer programs
- Remote patient monitoring
- EHR implementation for research
- Patient engagement initiatives
- eHealth-related:
- Pragmatic clinical trials
- Real-world evidence studies
- Implementation studies
Cancer informatics shared resource (CISR)
Leadership
The CISR, directed by Dr. Guo, is organized into three integrated units that address a broad range of informatics needs. The Clinical Data Integration & Discovery Unit, led by Drs. Song and Guo, provides research-ready data from EHRs, tumor registries, and clinical trial systems to support cohort discovery, data harmonization, and computable phenotype development. The Machine Learning and Generative AI Unit, led by Drs. Liu and Wu, collaborates with investigators on predictive modeling, causal inference, and NLP/LLM-based applications to advance precision oncology. The eHealth and Patient-Centered Informatics Unit, led by Dr. Gregory, enables the use of mobile technologies, PROs, and patient-generated health data (PGHD) in cancer research to enhance patient engagement and RWD capture.
Yi Guo PhD, FAMIA
Qianqian Song
Cancer informatics shared resource (CISR)
Staff
For CISR support or inquiries for Dr. Guo, contact Shuang Yang. For eHealth & Patient-Centered Informatics Unit support or inquiries for Dr. Gregory, contact Nicole C. Hammer.
Sonya H White
Clinical Data Integration & Discovery Unit
Led By Drs. Song and Guo, the unit provides UF researchers with research-ready data from EHRs, tumor registries, and clinical trial systems to support cohort discovery, data harmonization, and computable phenotype development.
clinical data integration & discovery unit
Services
Coming Soon in 2026
Expertise
The Clinical Data Integration & Discovery Unit provides expertise in:
Coming Soon in 2026
Machine Learning (ML) & Generative AI Unit
The Machine Learning (ML) & Generative AI Unit is the AI-enabled precision medicine arm of the CISR at the UF Health Cancer Institute.
Led by Drs. Liu and Wu, the unit collaborates with UF researchers on predictive modeling, causal inference, and NLP/LLM-based applications to advance precision oncology.
Machine Learning (ML) & generative ai unit
Services
The Machine Learning (ML) & Generative AI Unit helps UF researchers develop, validate, and deploy artificial intelligence and machine learning solutions that translate complex biomedical data into actionable insights. The unit supports predictive modeling, causal inference, natural language processing, and large language model (LLM) applications across multimodal data to enable AI-enabled precision medicine, improve clinical decision-making, and accelerate translational cancer research.
Expertise
The Machine Learning (ML) & Generative AI Unit provides expertise in:
AI/ML Model Development
- Risk prediction and prognostic modeling
- Disease sub-phenotyping and patient stratification
- Treatment response and toxicity prediction
- Model validation, calibration, and benchmarking
- Explainable and interpretable AI
Causal Inference & Real-World Evidence
- Retrospective observational comparative effectiveness analysis
- Target trial emulation
- AI-enabled clinical trial eligibility screening and matching
NLP & Generative AI
- Information extraction from biomedical text
- Automatic summarization, question answering, and chatbots
- Vision-Language Models (VLMs)
- Agentic AI reasoning
Multimodal Data Analytics & Learning
- Multimodal machine learning (MML)
- Data alignment and fusion
- Representation learning
eHealth & Patient-Centered Informatics Unit
The eHealth & Patient-Centered Informatics Unit is the digital health arm of the CISR at the UF Health Cancer Institute.
Led by Dr. Gregory, the unit enables UF researchers to design, implement and evaluate technology-enabled, patient-centered cancer research using mobile health (mHealth) tools, patient-reported outcomes (PROs), and patient-generated health data (PGHD) to enhance patient engagement and Real-world Data (RWD) capture.
eHealth & Patient-Centered informatics unit
Services
The eHealth & Patient-Centered Informatics Unit helps UF researchers move beyond traditional clinical data by integrating real-world patient experiences, symptoms, behaviors and outcomes into research and care delivery. Through digital platforms, the unit supports scalable, pragmatic and decentralized research approaches that improve patient engagement and data capture across the cancer care continuum.
Expertise
The eHealth & Patient-Centered Informatics Unit provides expertise in:
Digital Health Interventions
- Web and mobile health applications
- Patient portals and self-management tools
- Symptom trackers
- Clinical decision support tools
- Remote monitoring platforms
- Wearable devices
- Epic-based implementation
Patient-Reported Outcomes & Real-World Data Capture
- Survey and PRO development
- REDCap and app-based data collection
- Longitudinal symptom monitoring
- Integration of PGHD into research datasets
User-Centered Design & Implementation Science
- Prototyping and co-design with patients and clinicians
- Usability testing
- Workflow analysis
- Implementation planning
- Adoption and engagement strategies
Decentralized & Pragmatic Trial Support
- Remote data collection
- Virtual education/training tools
- Technology-enabled follow-up
- Reduced patient burden approaches
Resources
- Biomedical Informatics & Data Science (BMIDS)
- Cancer Research Data Commons (CRDC)
- Childhood Cancer Data Commons (CCDI)
- Exposome Database
- Florida Cancer Data System (FCDS)
- Florida Department of Health (FDOH)
- Health Outcomes & Biomedical Informatics (HOBI)
- HiPerGator
- Human Tumor Atlas Network (HTAN)
- Kaiser Permanente Southern California (KPSC)
- Malcom Randall Department of Veterans Affairs Medical Center (VAMC)
- Medical University of South Carolina (MUSC)
- National Cancer Institute (NCI)
- National Institutes of Health (NIH)
- NIH Public Access Policy
- Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)
- OneFlorida+ Data Trust
- Patient-Centered Outcomes Research Institute (PCORI)
- SEER-Medicare
- (Cancer Sites: Breast, Lung, Prostate, Colorectal, Pancreas, Lymphoma, Multiple Myeloma)
- The Cancer Genome Atlas (TCGA)
- UF Integrated Data Repository (IDR)
- U.S. Census
