# Teaching biomedical and pharmacological trainees to produce FAIR data for AI & ML applications

> **NIH NIH T32** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $74,383

## Abstract

PROJECT SUMMARY
The answers to many fundamental questions in medicine and biology currently lie buried inside data
collections that are too large and heterogeneous to be analyzed and visualized by traditional approaches. As
institutions such as the Mount Sinai Health System continue to make sizable investments in early detection of
diseases, expansion of more effective population health approaches, and drug/treatment protocol development
for personalized medicine, leveraging a wide range of data from imaging to pathology, from genomic data to
electronic health records, there is enormous pressure for rapid results. However, the capabilities of AI tools are
often overstated by vendors, and the deployment of these tools without sufficient training and understanding of
their validity and limitations can result in wasted resources and harmful patient outcomes.
Here we propose a supplement for T32 GM 062754, “Integrated Training in Pharmacological Sciences,” which
funds predoctoral trainees who focus on pharmacology while pursuing their PhDs in Biomedical Sciences at
Mount Sinai. With funds from this supplement, Dr. Hayit Greenspan, recently recruited to Mount Sinai from Tel
Aviv at the Professor level, will develop a course dedicated to data science for AI/ML in biomedicine. Support is
requested for a course module on competencies needed to make data FAIR and AI/ML-ready, and in the skills
required to collaborate effectively with researchers in information sciences and AI/ML.
The proposed new course synergizes extremely well with the existing efforts of the training program, which has
emphasized quantitative competencies for more than a decade. Although we are a program in pharmacological
sciences rather than in computational biology per se, all trainees are required to learn fundamental concepts in
programming, mathematical modeling, and systems pharmacology during the first year core curriculum. The
didactic focus, however, has been on mechanism-based mathematical models, with only more limited offerings
in AI and ML thus far. The new course will fill a substantial need in the current curriculum by educating our
predoctoral students on contemporary issues on data storage and availability that prevent AI/ML from reaching
its full potential in biomedicine. By teaching our students these issues and emphasizing the principles that
make data FAIR-compliant, we will facilitate future collaborations with data scientists and AI experts by
allowing groups with different perspectives to understand the relevant issues and speak the same language.

## Key facts

- **NIH application ID:** 10406034
- **Project number:** 3T32GM062754-21S1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** ERIC A SOBIE
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $74,383
- **Award type:** 3
- **Project period:** 2001-07-05 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406034

## Citation

> US National Institutes of Health, RePORTER application 10406034, Teaching biomedical and pharmacological trainees to produce FAIR data for AI & ML applications (3T32GM062754-21S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10406034. Licensed CC0.

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