# Graduate Training in Genetics

> **NIH NIH T32** · UNIVERSITY OF OREGON · 2021 · $78,315

## Abstract

Project Summary
In the era of big data, expertise in advanced statistical analyses including machine learning (ML) and artificial
intelligence (AI) is increasingly important for application to biological questions. In some cases, these
approaches can be applied by the biologist themselves, but in many cases collaboration between biological
researchers and ML/AI experts is needed. For these collaborations to be successful, streamlined
communication between experts in each field is essential. To accomplish this optimized communication goal,
biologists need a foundational understanding of ML/AI algorithms, how they can be appropriately applied to
biological datasets, and how biological experiments need to be designed for these ML/AI approaches. Here we
propose an intensive three-week workshop designed to teach trainees the fundamentals of ML/AI applications
to biological data. This workshop will synergize well with other existing courses, workshops and trainings
developed by The University of Oregon Presidential Initiative in Data Science that will also be available to
trainees interested in expanded trainings in ML/AI. The workshop will combine lecture components,
discussions of recent peer-reviewed literature, and hands on experience working with real data to train and
apply ML/AI algorithms. Week one will cover necessary fundamental topics including (1) What is machine
learning and Artificial Intelligence? (2) What are the most common algorithms underlying ML/AI analyses? (3)
What kind of data do I need to apply ML/AI? (4) How can I handle large datasets from repositories for data
mining and how can I make my data available on these databases? Lectures on fundamental ML/AI concepts
will be intermixed with the basics of data manipulation and principals of FAIR (Findable, Accessible,
Interoperable, and Reusable) data management. Week two will cover common ways data from various next
generation sequencing technologies - including single-cell sequencing data - can be analyzed using ML/AI and
will include hands-on training. Week three will focus on applying ML/AI to image analysis, including training on
the analysis and annotation of image data, manipulating and transforming image files, and training a neural
network image classifier to automate lab processes. By the end of the workshop, trainees will have the
foundational skills needed to collaborate with ML/AI experts, ask new research questions about existing data
and design powerful experiments, and explore novel research directions through applications of ML and AI.

## Key facts

- **NIH application ID:** 10406063
- **Project number:** 3T32GM007413-44S2
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** Karen J Guillemin
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $78,315
- **Award type:** 3
- **Project period:** 1977-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406063

## Citation

> US National Institutes of Health, RePORTER application 10406063, Graduate Training in Genetics (3T32GM007413-44S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10406063. Licensed CC0.

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