Bioinformatics Training & Education Program

Microsoft Visual Studio Code (VS Code) is an integrated development environment (IDE) for writing scripts and viewing as well as organizing output from coding projects. It is compatible with languages and tools including R, Python, Jupyter Notebook, Quarto, Julia, C++, Matlab, HTML, and markdown. After attending, participants will know the benefits of using VS Code including AI assistance and Git project versioning integration, how to start coding projects, and how to access VS Code. Experience is not needed to participate.

 

This one-hour online training introduces attendees to modeling and simulation of biological systems using MATLAB’s SimBiology and BioPipeline Designer toolboxes. SimBiology is a versatile toolbox for modeling, simulating, and analyzing dynamic biological systems such as metabolic pathways, signaling cascades, and pharmacokinetics/pharmacodynamics (PK/PD) models. BioPipeline Designer complements this by streamlining workflows for integrating biological data and automating computational analyses. 

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

• Describe the capabilities and applications of SimBiology and BioPipeline Designer for modeling and analyzing biological systems. 

• Construct and parameterize basic models of biological processes using SimBiology’s graphical and programmatic interfaces. 

• Simulate dynamic behaviors of biological systems, such as time-course analyses, and interpret simulation results. 

• Automate and streamline data integration workflows using BioPipeline Designer to enhance reproducibility and efficiency. 

• Access and utilize resources for further learning, including tutorials, user guides, and MATLAB community forums 

Attendees are expected to be familiar with the basic functions of the MATLAB to be successful in this training. 

Think prompt engineering is the key to better AI results? Think again. The most effective AI users know that what you tell AI matters—but what you give it matters even more. Join CIT’s Technology Training Program for Context Is King: The Secret Sauce to Great AI, an engaging class designed to help you get more useful, accurate, and relevant results from AI tools. You’ll discover why context engineering has become one of today’s most valuable AI skills. Through practical, NIH-focused examples, you’ll learn how to provide the background, goals, source materials, examples, audience information, and constraints that help AI understand what you really need.

In this class, you’ll learn how to:

• Recognize why even a well-written prompt can produce a weak response
• Give AI the background and supporting information it needs
• Define your audience, goals, requirements, and boundaries
• Use examples and documents to improve accuracy and relevance
• Build reusable context for common NIH tasks
• Help AI respond more like a knowledgeable teammate—and less like a generic chatbot

How are AI foundation models expanding the possibilities of computational pathology?

 During the upcoming Data Science Seminar series, Dr. Andrew Song from UT MD Anderson Cancer Center will tackle this question and explore how the answer could be applied across modalities.

 He will:

• highlight the growing role of foundation models in computational pathology and how they can support scalable representation learning and flexible analytical workflows.
• explore how AI models can extend beyond histopathology to incorporate modalities such as spatial transcriptomics and proteomics.
• discuss how multimodal deep learning approaches can help researchers connect information across tissue images and molecular data to gain new insights into disease biology.

 Register and learn how researchers are developing models that work across different data modalities and spatial scales.

This 90-minute online roundtable explores practical applications of artificial intelligence (AI) in statistics and data analysis across the NIH research landscape. Brief presentations from panelists representing statistical, data science, and research perspectives will be followed by an open moderated discussion. Attendees will come away able to identify real-world AI use cases in research workflows, describe the opportunities and limitations of AI-assisted methods, and discuss how AI may shape the future of statistical practice, biomedical research, and decision-making at NIH and HHS. The discussion will also touch on considerations of bias, reproducibility, and responsible AI use within federally-funded research contexts.

Generative AI becomes useful for science when it is paired with domain knowledge, explicit context, executable checks, reusable instructions, and human judgment. In this session, we will go on a practical journey from scripts to reproducible AI-assisted bioinformatics, designed for wet-lab scientists and researchers who are early in their computational practice.

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.

This hour and a half online training covers how to analyze and model data using interactive tools in MATLAB. Through live demonstrations and examples, attendees will learn to solve many steps in a data analysis workflow without writing any code. The interactive tools can generate the MATLAB code needed to reproduce the work programmatically. 

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

• Use interactive tools for data visualization, cleaning, and modeling
• Automatically generate code to replicate interactive work
• Capture work in easy-to-write scripts and functions
• Share results by automatically creating reports

This training taught by MathWorks. Attendees are not expected to have any prior knowledge of MATLAB, but experienced users will also benefit from new tools, tips, and tricks from the latest releases. This training is an introductory level; no software installation required.

LC-MS/MS is widely used in exposomics studies. MetaboAnalyst (https://www.metaboanalyst.ca/) is a widely used platform for statistical and functional analysis of metabolomics data. The current MetaboAnalyst 6.0 release incorporates updates to meet the needs of exposomics studies, including improved support for tandem mass spectrometry compound identification, exposome annotation, dose–response analysis and linking to genetics and functions. 

Generative AI models are powerful tools that can enhance research, but they also pose risks that can be detrimental to our work. This session will focus on responsible use of AI tools for Bioinformatics to help researchers get the most out of them while avoiding common pitfalls. We'll cover best practices for AI use in a research setting including reproducibility, documentation, and validation for responsible integration of AI into research workflows.

This session will present a case study showing how generative AI can support a real analysis workflow while preserving scientific rigor, validation, and reproducibility. It will also discuss caveats and mistakes to learn from.

Dr. Marylyn D. Ritchie is Chief Artificial Intelligence Officer for the MUSC Enterprise and Director of the MUSC AI Center for Health Innovation and Informatics. She also serves as Associate Dean for Artificial Intelligence and SmartState Endowed Chair in Translational Biomedical Informatics. An expert in translational bioinformatics, Dr. Ritchie develops methods integrating electronic health records with genomic data to advance research and patient care. She has more than 20 years of experience and over 500 publications. Dr. Ritchie is a Fellow of the American College of Medical Informatics and was elected to the National Academy of Medicine in 2021.

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.

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. 

Bridge2AI Fall 2026 All Hands Meeting & Open House
October 20–21, 2026 | Hilton Washington DC/Rockville Hotel & Executive Meeting Center

Registration is open for the Bridge2AI Fall 2026 All Hands Meeting & Open House. The event will highlight the latest advancements from the NIH Common Fund’s Bridge2AI program, including AI‑ready flagship datasets, tools and standards, and training resources developed to expand machine learning applications in biomedical research. Participants will have opportunities to engage with consortium teams, explore new resources, and participate in hands‑on learning sessions.

Event details and registration are available at:

• Event Website: https://web.cvent.com/event/3f4cbe6f-4006-4f79-a2ba-2c4fbf77bae3/summary

Registration closes October 6, 2026.

Dr. Jiang’s research focuses on developing data-integration and artificial intelligence frameworks to study intercellular signaling mediated by secreted proteins in anti-tumor immunity. Data-driven analyses estimate that about two thousand human genes encode secreted proteins. Yet, literature mining reveals that 61% of these genes lack known roles in cancer. To address this gap, his lab developed computational methods and applied diverse immunological models to dissect cytokine networks, secreted proteins, and ligand–receptor interactions in cancer. Ultimately, the labs’ goal is to uncover new mechanisms of immune regulation and identify therapeutic opportunities that harness intercellular communication against tumors.

This 30-minute online training provides a high-level overview of recent developments in artificial intelligence (AI). Each session highlights emerging trends, tools, and use cases in the evolving AI landscape, with an emphasis on practical relevance and responsible use. Whether you're just getting started or looking to stay current, this training offers timely insights in a concise format.  

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

• Summarize key trends and developments in AI 

• Identify new tools, capabilities, or applications relevant to their work 

• Describe considerations for ethical and responsible use of AI technologies 

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

At this time, this workshop is only for NIH-affiliated staff & scientists only. Please use your NIH email when registering. 

Join Allen Institute scientists for a one-day, hands-on workshop at NIH focused on helping researchers explore and use cell type taxonomy data and tools from the Brain Knowledge Platform. The workshop will begin with an accessible overview of what cell types are and how they are defined, followed by guided tutorials and interactive exercises using resources for exploring open datasets such as the Allen Brain Cell Atlas. Designed to be highly interactive and applicable across experience levels, the workshop will combine short presentations with individual and guided hands-on time to help participants build practical skills and confidence in applying these tools to their own research and teaching. 

Breakfast, coffee/tea, and lunch will be provided. Registration is free, but required. There is a hybrid option for lectures only, please still register if you need the remote option.

Agenda:
9:00-9:30 Check-in / breakfast

9:30-9:40 Welcome, logistics, overview of the Allen Institute 9:40-10:05 Cell typing at the Allen Institute: taxonomies, data sets, and use cases 10:05-10:30 Deep-dive into the human and mammalian brain atlas 10:30-10:40 Break 10:40-11:05 Localizing cell types with spatial transcriptomics 11:05-11:30 Cell-type targeted enhancer development 11:30-11:55 Deep-dive into the Seattle Alzheimer's disease brain cell atlas 11:55-12:10 Overview of Brain Knowledge Platform tools and resources 12:10-12:15 Preview of afternoon tracks 12:15-1:15 Lunch --- Breakout session 1: Connecting multimodal data to brain cell types 1:15-1:35 Defining and using common coordinate frameworks  1:35-1:55 Patch-seq overview and tools for analysis  1:55-2:30 Additional multimodal datasets from Allen  --- Breakout session 2: Analysis of your own sequencing data using Allen Institute resources 1:15-1:45 Cell type analysis, taxonomy creation, and mapping  1:45-2:10 Cross-species alignment and comparative analyses  2:10-2:30 Other interactive tools for -omics data exploration   --- 2:30-2:40 Break 2:40-3:00 ABC Atlas demo & external use case examples 3:00-3:30 User exploration of Allen Institute tools  3:30-3:45 Tool feedback and Discussion 3:45-4:00 Workshop Survey / closing of formal program 4:00-5:00 Additional time for user exploration