Classes & Events

This one-day in-person NIH AI Symposium will bring together researchers from a broad range of disciplines to share their AI-related research, with the goal of disseminating the newest AI research, providing an opportunity to network, and to cross-pollinate ideas across disciplines in order to advance AI research in biomedicine. We welcome all NIH researchers who are interested in AI, from novices to experts.

Sponsored by NHLBI and the Office of Intramural Research. 

Keynote Speakers: 

• Dr. Alexander Rives, Co-founder and chief scientist at Evolutionary Scale, a company focused on applying machine learning and language models to biological systems, including the development of ESM3, a protein language model that enables the generation of novel proteins with potential applications for drug discovery and basic biological research. 
• Dr. Leo Anthony Celi, Senior Research Scientist at Massachusetts Institue of Technology (MIT) and Associate Professor of Medicine at Harvard Medical school, who has a broad range of interests including integrating clinical expertise with data science, using information technology to enhance healthcare in low- and middle-income countries, and considering the social impacts of AI research. 

About Event: Biomedical science is in the early phase of a technological revolution, driven in large part by innovations in deep learning neural network architecture and availability of computational power. These cutting-edge techniques are being applied to every sub-field of the biological sciences, and with novel ground-breaking advancements arriving every week it is challenging for researchers to stay up to speed on what is available and possible.

Please register and submit a poster abstract. Attendance is limited, so please register now to reserve your spot. 

Registration deadline: April 25, 2025
Abstract deadline: April 11, 2025

Immunotherapy has the potential to revolutionize the way we treat cancer, but a key challenge has been finding ways to tailor that therapy to the right patient at the right time.

Attend this webinar, hosted by Dr. Li Zhang, of the University of California-San Francisco, to learn about a platform that can help you gain critical insight into immune cell responses to plan more effective and targeted treatments.

The platform, called Network Analysis of Immune Repertoire (NAIR), funded by NCI’s Program for Informatics Technology Development, gives you access to pipelines, tools, and resources for examining T-cell receptors (TCRs) and linking those immune responses to gene expression.

With NAIR you can use both bulk and single-cell sequencing data to:

• identify critical cancer-related TCR clusters and explore shared public clusters across multiple samples to identify potential disease-specific signatures.
• integrate single-cell gene expression data using a Graph deep learning model to delve deeper into T cell functionality.
• predict binding peptides by integrating TCR sequence vectorization, V/J gene, and HLA genotype data within NAIR’s deep learning framework.
• unlock the complex interplay between adaptive immune system, disease progression, and clinical outcomes to better understand immune system dynamics.

In this talk, Dr. Hwang will discuss three examples of applying single-cell and spatial oncology approaches to the study of pancreatic cancer. First, he will explain how they utilized single-nucleus RNA-sequencing to uncover an enriched neural-like progenitor malignant state after cytotoxic therapy. Second, he will detail the development and application of spatially-constrained optimal transport interaction analysis (SCOTIA) to reveal augmented IL6 signaling between cancer-associated fibroblasts and malignant cells in residual disease. Third, he will present their use of spatial transcriptomics to study cancer-nerve interactions, which identified PDGFD-PDGFRB signaling as a mediator of intratumoral neuroinvasion. Finally, he will discuss his thoughts on the future of spatial oncology and ongoing efforts in spatial multi-omics.

The programming language R is ideal for biomedical researchers as it has packages that facilitate Next Generation Sequencing (NGS) data analysis. For example, bulk RNA sequencing differential expression analysis can be performed with DESeq2 and Seurat is used for analyzing single cell RNA sequencing data. When programming, scientists are encouraged to keep track of versions using tools such as Git (https://git-scm.com). Git is a software that saves the history of code, which enables scientists to track and revert changes. This Coding Club will introduce participants to versioning using Git inside of R Studio, a graphical interface for working with R. Essential steps in versioning code such as setting up Git in R Studio, tracking code history, reverting to previous versions, and sharing code on GitHub will be covered. After this class, participants will appreciate the convenience of versioning using Git within R Studio and start to apply materials learned to track changes in their own R scripts. This class is a demo and not hands-on. Attendance is restricted to NIH staff.

Register at: https://cbiit.webex.com/weblink/register/r2db7aaaad07c6fc543057da626f9d50a

It took over $3 billion and 13 years to sequence the first human genome. Today, we can sequence a genome in a single day for less than $1,000.  That incredible technological advancement has led to the generation of petabases of genomic data every year, equivalent to sequencing millions of human genomes annually.  Computational genomics, the process of analyzing these massive datasets, is the essential link that transforms the flood of raw information into usable insights to address human health challenges, agricultural inefficiencies, wildlife conservation, and more.  In this webinar, we will discuss the breadth of topics under the computational genomics umbrella and how they connect to real-world innovations.

Agentic AI is a class of artificial intelligence that acts autonomously to make decisions and take actions to achieve specific goals. During this event, the participants will discuss the emerging role of AI agents as intelligent partners in cancer research.

The virtual Cancer AI Conversations Series features perspectives on timely topics and themes in artificial intelligence for cancer research.

Each event features short talks from a panel of subject matter experts, offering diverse views on the session topic. These talks are followed by a moderated panel discussion.

Moderator: Anwesha Dey, Ph.D., Genentech
Panelists: James Zou, Ph.D., Stanford University and Vivek Natarajan, Ph.D., Google

Additional information can be found on the Cancer AI Conversations website.

If you are an individual with a disability who needs reasonable accommodations to participate in this event, please contact Dr. Juli Klemm at your earliest convenience.

This one-hour online training will cover the fundamentals, applications, and ethical considerations of Artificial Intelligence (AI). Attendees will explore key topics such as machine learning, deep learning, data handling, and real-world AI applications across various industries. The session will also delve into the ethical implications of AI and provide insights on becoming AI literate. Whether you're a seasoned professional or just starting your AI journey, this session will equip you with essential knowledge to navigate the AI landscape effectively and make informed decisions in our data-driven world.

By the end of this training, attendees will be able to: 

• Understand the core concepts of AI 
• Recognize the significance of ethical considerations in AI 
• Begin the journey toward AI literacy

Attendees are not expected to have any prior knowledge of AI to be successful in this training. 

Prostate cancer exhibits significant intratumoral heterogeneity, driving a spectrum of phenotypes ranging from indolent disease to aggressive metastasis, challenging risk stratification and treatment. A key feature of this heterogeneity is clonal diversity, encompassing both cancer cells and their interactions with the surrounding stromal microenvironment. Understanding clonal dynamics and stromal influences on tumour progression is crucial for identifying factors that contribute to metastasis and disease lethality, ultimately improving risk stratification and therapeutic strategies.

Leveraging spatial transcriptomics, we have identified clonal subtypes across biopsy, primary and metastatic tissues, revealing distinct clonal relationships and polyclonal lymph node colonisation at different evolutionary stages. These detailed molecular maps are complemented by spatial profiling of the tumour microenvironment to understand how stromal context influences tumour behaviour.

To overcome the limitations of 2D histopathology, we are developing 3D imaging approaches using open-top light-sheet (OTLS) microscopy, enabling full-volume visualisation of glandular architecture, vasculature, and cellular organisation. Together, these complementary approaches provide insights into tumour heterogeneity and metastatic potential. By integrating molecular and structural data into a unified 3D framework, we aim to refine biomarker discovery and support more accurate clinical decision-making.

Class Description

• Introduction to RNA-Seq data analysis
• Step-by-step live demonstration of RNA-Seq analysis using the Galaxy platform

 What You’ll Learn:

• How to independently carry out the basic gene expression profiling workflow
• Hands-on experience with Illumina paired-end RNA-Seq data through self-paced exercises

Whether you’re new to bioinformatics or looking to sharpen your RNA-Seq data analysis skills, this workshop will equip you with practical tools and confidence to run your own analyses – no programming required.

This is the first class of the Python Introductory Education Series. Here, participants will learn how to access and interact with Python, obtain an understanding of command syntax for this programming language, know how to get help with Python commands, and be familiar with where to find Python external packages. Experience with Python is not needed for attendance. Participants are required to have access to Biowulf and this class is restricted to NIH staff.

Registration: https://cbiit.webex.com/weblink/register/r3588d958cd4c9965d59e7494b1799b3a

This one hour and half hour online training will equip participants with essential knowledge and skills for effective interactions with Large Language Models (LLMs), such as ChatGPT. Explore the intricacies of prompt engineering and its pivotal role in optimizing the conversational capabilities of LLMs. Emphasizing best practices and practical applications, this training features live demonstrations and group discussion, and provides valuable skills for the effective use of LLMs. 

By the end of this training, attendees will be able to:  

• Define LLMs, prompt patterns, and prompt engineering
• Identify potential uses and issues to consider when using LLMs in the biomedical research field
• Use a selection of prompt patterns to improve generated output from LLMs
• Identify resources for learning more about prompt engineering in LLMs 

Attendees are not expected to have any prior knowledge of ChatGPT to be successful in this training. 

In the second class of the Python Introductory Education Series, participants will start to dive into Python. Participants will walk away with knowledge of common Python data types and structures as well as how to assign variables, understand conditionals, and perform repetitive tasks using loops or iterators. Experience with Python is not needed for attendance. Participants are required to have access to Biowulf and this class is restricted to NIH staff.

Registration: https://cbiit.webex.com/weblink/register/r81543a0b3775bc93dbf98aecaf10fd5d

This class of the Python Introductory Education Series will introduce participants to working with and wrangling tabular data using the package, Pandas. Experience with Python is not needed for attendance. Participants are required to have access to Biowulf and this class is restricted to NIH staff.

Registration: https://cbiit.webex.com/weblink/register/r34b119afdb21840a74ea5ad858ae283f

This two-hour virtual roundtable discussion will cover development and implementation of artificial intelligence (AI) chatbots at NIH. A chatbot is a software application or web interface designed to have textual or spoken conversations and may use generative AI systems, and chatbots are being developed at NIH to support both intramural and extramural biomedical research. The program will begin with brief presentations by our panelists, followed by an open discussion.  

By the end of this training, attendees will be able to: 

• Identify use cases for AI chatbots at NIH
• Discuss emerging trends and techniques for development of AI chatbots at NIH
• List resources and tools for learning about, using, and developing AI chatbots at NIH

Attendees are not expected to have any prior knowledge of AI chatbot development.

Presenters:

Alicia Lillich, NIH Library 
Generative AI Chatbots in the NIH Landscape: Foundations, Opportunities, and Considerations

Steevenson Nelson, Ph.D., OD
Chatbox for the Intramural Research Program (ChIRP)

Trey Saddler, NIEHS
ToxPipe: Chatbots and Retrieval-Augmented Generation on Toxicological Data Streams

Faraz Faghri, NIA
CARDbiomedbench: Biomedical benchmark of chatbots, CARD.AI Arena, CARD.AI, FAIRkit

Dianne Babski, NLM
AI Chatbots: Opportunities and Considerations at NLM

This class will wrap up the Python Introductory Education Series by showing participants how to create data visualizations. Experience with Python is not needed for attendance. Participants are required to have access to Biowulf and this class is restricted to NIH staff.

Registration: https://cbiit.webex.com/weblink/register/rc4ff8b251451d806103bc025ab752e60

This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.  

By the end of the training, attendees will be able to: 

• Recognize four freely available IDEs for python coding 

• Identify fundamental components of python code 

• Understand how and why notebooks support rigor and transparency in analysis 

Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.  

If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option. 

This 45-minute online Lunch and Learn training will help attendees develop their own customized strategy for responsibly incorporating generative artificial intelligence (AI) tools, such as ChatGPT, into their workflows. 

By the end of this training, attendees will be able to: 

• Assess appropriate use cases for generative AI tools within their specific research/work context 
• Develop a customized generative AI usage strategy 
• Document their approach for using generative AI tools 

Attendees are not expected to have any prior knowledge of generative AI tools to be successful in this training. 

This one-hour online training provides an introduction on how to sign up and access complimentary SAS training resources available to NIH and HHS employees. 

By the end of this training, attendees will be able to:  

• Enroll in recommended SAS 9.4 trainings and courses
• Navigate complimentary SAS tutorials, programming courses, and eLearning 

Attendees are not expected to have any prior knowledge of SAS to be successful in this training. 

This one-hour and half minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.   

By the end of part one of this training series, attendees will be able to:   

• Understand data management best practices   
• Become familiar with data management tools  
• Have a solid knowledge of the resources, enabling data sharing  

During Part 2, attendees will learn about sharing and archiving data. You must register separately for Part 2 of this training. This training is introductory, no prior knowledge required.  

This one-hour and fifteen minute online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.  

By the end of part two of this training series, attendees will be able to:   

• Have a solid knowledge of the resources, enabling data sharing  
• Understand how data is archived and preserved  

Part 1 of this training covers understanding research data, how to manage research data, and how to work with data. During Part 2, attendees learn about sharing and archiving data. This training is introductory, no prior knowledge required.  

You must register separately for Part 1 of this training.  

NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data.

Learning Objectives

1. The learner should know the difference between observational studies, clinical trials (drug and non-drug studies), and secondary data (new data from stored samples, existing data) as defined for the NIH Clinical Center and how study development differs for each.

2. The learner should understand the development process, know the timeline, and know the resources available for successful protocol development.

3. The learner should understand the purpose and scope of ClinicalTrials.gov.

4. The learner should be able to identify and understand key data elements and each step of trial registration and reporting.

5. The learner should be able to understand the differences between a scientific hypothesis and a statistical hypothesis.

6. The learner should be able to translate scientific hypotheses into statistical design elements: study design, primary outcomes, statistical hypotheses, sample size calculation, and statistical analysis plan.

Tentative Webinar Outline:

2:30-3:00pm – Nancy Alexander (Nurse Specialist (Research), Protocol Navigation Program Lead, NIDDK)

Research study types, timelines, and process for successful protocol development, IRB approval, and study initiation at the NIH, with particular emphasis on NIDDK resources and processes.

3:00– 3:30pm – Speaker TBA

Understanding ClinicalTrial.gov elements and how they are used in trial registration and reporting for studies at the NIH.

3:30-4:00pm – Dr. Sungyoung Auh (Mathematical Statistician, Biostatistics Program Office, NIDDK)

Translating scientific questions to needed statistical design elements for research study planning, documentation, completion, and reporting.

NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data.

Learning Objectives:

1.         To delineate features of REDCap to support project management for research studies.

2.         To outline steps to create detailed data collection plans which fulfill regulatory requirements. 

3.         To identify principled approaches to data collection and management. 

4.         To explain the connections between research rigor and reproducibility.

Outline:

2:30-3:00pm – Matthew Breymaier (Informatics Specialist, Office of the Clinical Director, NIDDK), Sai Theja (Senior Data Analyst, Office of the Clinical Director, NIDDK)

RedCap – functionality and basics of setup and how different types of studies can be designed in RedCap (longitudinal vs cross-sectional etc), with emphasis on NIDDK RedCap.  

3:00– 4:00pm – Dr. Kenneth Wilkins (Mathematical Statistician, Biostatistics Program Office, NIDDK)

Document organization and access as part of study planning: regulatory, clinical, and case report forms 

Data Management and Sharing Plans

Data Management for Reproducibility

Learning Objectives:

Be able to identify, load, and use R resources/packages based upon needs and experience level with R.  

1. For beginners, know how to load R Commander, import data, and navigate the GUI.  

2. For those interested in learning more about coding/functions, how to use R Swirl to learn foundations for functions and coding higher level operations (loops, combining functions, and building new functions).  

3. For regular users of R, how to use tidyverse for data manipulation, organization, and preparation for analysis.  

4. For those using R for research work, how to utilize R Markdown for appropriate and thorough project documentation and management.  

Outline:

2:30-3:00pm –Beginner level (Dr. Wilkins, Mathematical Statistician, Biostatistics Program Office, NIDDK)

How to get the basics accomplished: load data, navigate RCommander GUI, and export data.

3:00– 3:30pm – Intermediate level (Dr. Leary, Chief, Biostatistics Program Office, NIDDK)

Data manipulation and organization for analysis with focus on tools for more complex coding and functionality.

3:30-4:00pm – Advanced topics (Dr. Leary)

Leveraging R Markdown and other resources for project management, documentation, and archiving.