# Cancer Research Workforce Development in FAIR Artificial Intelligence and Machine Learning

> **NIH NIH T32** · H. LEE MOFFITT CANCER CTR & RES INST · 2021 · $86,337

## Abstract

Given the rapidly growing collection of clinical and molecular data available for cancer and other
diseases, the potential application of artificial intelligence (AI) and machine learning (ML)
approaches applied better and more cost-effective clinical decision making is compelling.
However, the success of the application of AI/ML algorithms, especially in the clinical domain,
hinges on the availability and quality of data for training and validation of the AI/ML models.
Therefore, an unmet need is in the development of competencies and skills needed to make
biomedical data ready for AI/ML applications that can meet the four FAIR requirements:
Findable, Accessible, Interoperable, and Reusable. This supplement to our Integrated Program
in Cancer Data Science addresses this need by developing a short course in FAIR application
that will be distributed in three venues: 1) a short-course for Ph.D student offer through the
University of South Florida, which host our Cancer Biology PhD Program, 2) hands-on
workshops for postdoctoral fellows and other early-staged investigator at the Moffitt Cancer
Center, and 3) public dissemination of videotaped lectures via Moffitt YouTube channel.

## Key facts

- **NIH application ID:** 10405929
- **Project number:** 3T32CA233399-03S1
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** William Douglas Cress
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,337
- **Award type:** 3
- **Project period:** 2019-09-16 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10405929

## Citation

> US National Institutes of Health, RePORTER application 10405929, Cancer Research Workforce Development in FAIR Artificial Intelligence and Machine Learning (3T32CA233399-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10405929. Licensed CC0.

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