Bioinformatics Training & Education Program

Dr. Barzilay’s research in the intersection of AI and healthcare is unparalleled. Her research has been vital in expansion of machine learning and use of natural language processing in medicine. Her early breast cancer diagnosis tool is also being tested and used in multiple hospitals around the world. Her research now focuses on bringing the power of machine learning to oncology. This includes disease detection, drug discovery and the development of medical devices.

Discover how Microsoft 365 Copilot can help you create, manage, and enhance SharePoint sites and pages more efficiently. Learn how to use AI-powered tools to generate content, improve collaboration, organize information, and build engaging SharePoint experiences that save time and boost productivity.

Topics will cover graph-based deep learning and optimal transport approach modeling spatial heterogeneity in kidney spatial transcriptomics data, interpreting cellular graphs, and applying computational methods to kidney diseases.

This two-day national symposium addresses recent advances in the field and should be an exciting forum for discussion and debate on the current understanding of cancer immunology in the era of omics and artificial intelligence.

Confirmed Speakers:

• Grégoire Altan-Bonnet, NCI
• Avinash Bhandoola, NCI
• Remy Bosselut, NCI
• Mary Carrington, NCI
• Leah Cook, NCI
• Amiran Dzutsev, NCI
• Donna Farber, Columbia University
• Paul François, Université de Montréal
• Romina Goldszmid, NCI
• Timothy Greten, NCI
• Peng Jiang, NCI
• Yann LeCun, New York University
• Lichun Ma, NCI
• Bali Pulendran, Stanford School of Medicine
• Barbara Reherman, NIDDK
• Eytan Ruppin, Cedars-Sinai Medical Center
• Eldad Shulman, Cedars-Sinai Medical Center
• Naomi Taylor, NCI
• Giorgio Trinchieri, NCI
• John Tsang, Yale University
• Roxane Tussiwand, NCI 
• Golnaz Vahedi, University of Pennsylvania School of Medicine
• Roberto Weigert, NCI
• Ramnik Xavier, Harvard University
• Li Yang, NCI
• Chen Zhao, NCI
• Marlies Meisel, University of Pittsburgh School of Medicine
• Rosandra Kaplan, NCI

Main Topics

• DATA SCIENCE AND DEEP LEARNING IN CANCER IMMUNITY
• TUMOR MICROENVIRONMENT
• MICROBIOME AND CANCER
• T CELLS IN CANCER IMMUNITY

You know the basics of prompt engineering—but great AI results require more than writing better prompts. In this course, you'll learn the advanced techniques that separate casual AI users from AI power users. Discover how to refine and troubleshoot prompts, guide AI through complex tasks, structure outputs for higher quality, and use AI as a strategic thinking partner rather than just a content generator. Through practical NIH-focused examples and hands-on exercises, you'll explore prompt optimization, multi-step prompting, critical thinking frameworks, and methods for improving accuracy, clarity, and usefulness.

Presenting the third and final event in a 3-part series on Project and Data Management; created and presented by the NIDDK Biostatistics Program and the Office of the Clinical Director.

Learning Objectives:  

To understand best practices for research safety monitoring and reporting

To reflect on the importance of ongoing data review to ensure data integrity

To review successful strategies for timely reporting of clinical data in CT.gov for regulatory compliance 

 

No one fights cancer alone — not patients, and not scientists. Every discovery at the Frederick National Laboratory for Cancer Research builds on the shared efforts of researchers, clinicians, and computational biologists working towards a common goal. From high-throughput sequencing of complex experimental designs to downstream computational analysis, this talk highlights how the Center for Cancer Research’s Collaborative Bioinformatics Resource (CCBR) team leverages high-performance computing to transform raw sequencing data into meaningful biological insights. This behind-the-scenes view of collaborative,data-driven genomics highlights how rigorous analysis and reproducible accelerates discoveries that move us closer to better prevention, diagnosis, and treatment.

This one-hour online training provides researchers with an overview of online resources for locating research datasets, data repositories, and data publications for data sharing and re-use. Participants will learn search strategies for locating datasets through federated data search portals and generalist data repositories, including directories for locating discipline-specific and institutional data repositories. An overview of key issues to consider when re-using datasets or when locating a data repository for sharing and preservation purposes will be discussed. 

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

• Locate different types of data repositories and datasets 

• Identify issues to consider with data repositories 

• Discuss how data repositories can improve reproducibility

• Identify issues to consider when re-using datasets 

• Describe guidelines and resources for citing datasets 

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

General Schedule of Events

https://researchfestival.nih.gov/2026/general-schedule-events-0

 

Claude 101 is part 1 of a two-part series. 

This hour and half online training led by Anthropic will cover the fundamentals of using Claude effectively in your daily NIH workflows. Attendees will learn to navigate the Claude interface, apply best practices for prompt writing, and utilize key features such as working with documents, Projects, and Artifacts. The training will also demonstrate real-world use cases relevant to NIH staff for improving productivity, and highlight security and responsible-use considerations tailored for federal environments. 

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

• Navigate the Claude interface and use foundational features, including working with documents, Projects, and Artifacts. 

• Apply effective prompting strategies to generate accurate, useful outputs for NIH-specific tasks. 

• Identify everyday NIH use cases and understand best practices for responsible use of generative AI tools like Claude. 

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

Claude 201 is part 2 of a two-part series. 

This hour and half online training led by Anthropic will dive deeper into intermediate and advanced strategies for maximizing Claude in NIH workflows. Building on the fundamentals from Claude 101, this training will focus on structured and multi-step prompting, working effectively with longer documents and datasets, and using Projects to organize ongoing work and build reusable context. Attendees will also learn how to integrate Claude into specialized NIH tasks and optimize outputs for research, administrative, and policy workflows. 

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

• Use structured and multi-step prompting techniques to handle complex tasks and improve output quality. 

• Work effectively with documents, longer-form content, and data inside Claude to support research and analysis workflows. 

• Set up and use Projects to organize ongoing work, build reusable context, and collaborate on NIH-specific initiatives. 

Attendees are expected to be familiar with the basic functions of Claude to be successful in this training (gained by attending Claude 101, attending another relevant training, and/or using Claude previously). 

Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of code-based NGS analysis. This class is demonstration-only. Starting from single cell RNA expression matrix, Illumina scientist will illustrate how to conduct QC, perform cell type classification, obtain differential expression results, and generate visualizations. No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.

Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of code-based NGS analysis. In this demonstration-only class, an Illumina scientist will show a bulk ATAC-sequencing workflow starting from FASTQ files through peak and motif detection as well as comparison of peaks found across samples. No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.