Classes & Events

This introductory lecture presents data classification as a statistical-learning topic at the intersection of statistics, computer science, and engineering. It emphasizes the predictive-performance culture of classification while introducing core concepts, such as predictor variables (or features) and output variables (or labels), binary and multiclass classification, class-probability prediction, model fitting, and model validation. The lecture will cover practical performance assessment using accuracy, sensitivity, specificity, and related notions. It will also briefly introduce some frequently used classifier families, such as the K-nearest-neighbors (KNN) classifier and generalized linear models (including logistic regression). Attendees should have a beginner level of statistical knowledge, intermediate is preferred.

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.

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.

Single cell gene expression profiling has demonstrated that cell classification requires more than a simple collection of marker genes. Current approaches do not account for the dynamic nature of cell states and inherent variation in cell types. This is especially true in the central nervous system (CNS) where morphological, location, and activity- based definitions of cell types fail to correlate well with single cell transcriptional profiles. Given how cell these cell extrinsic features are essential for the collective behavior and functionality of individual cells, it is critical to resolve how cell intrinsic molecular programs interact with and affect cell extrinsic physiology. 

Dr. Migliozzi's lab is focusing on dissecting the glioma eco-system during evolution through single cell and spatial transcriptomics to identify new therapeutic targets for the GBM subtypes which still lack therapeutic options. Her long-term research goal is to understand how tumor microenvironment affects the functional cellular states of GBM during its progression and utilize this knowledge to inform more effective therapies to this currently incurable disease.

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. 

Qlucore Omics Explorer is a desktop-based point-and-click software with built-in machine learning capabilities. It also enables RNA sequencing (bulk and single cell) and metabolomics analysis. This software is available for NCI CCR scientists upon submitting a ticket at https://service.cancer.gov/ncisp. In this demonstration-only class, participants will learn to build a classifier based on a leukemia transcriptomics dataset and use the classifier to predict in which groups samples from another leukemia dataset are likely to belong.

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.

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. 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. 

Qlucore Omics Explorer is a desktop-based point-and-click software with built-in machine learning capabilities. It also enables single cell RNA sequencing and metabolomics analysis. This software is available to NCI CCR scientists upon submitting a ticket at https://service.cancer.gov/ncisp. In this demonstration-only class, participants will learn to use regression approaches to identify correlations between gene expression and methylation data. Experience using or installation of this software is not required for attendance.

This one-hour online training introduces participants to predictive modeling techniques for evaluating the toxicity of small molecules using MATLAB. Participants will explore the principles of cheminformatics, learn how to preprocess molecular data, and build predictive models using machine learning. The training highlights MATLAB's specialized tools for feature extraction, model development, and performance evaluation tailored to small-molecule datasets. Designed for researchers and data scientists in fields such as drug discovery and toxicology, this session equips attendees with practical skills for leveraging AI in molecular analysis.

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

• Understand the role of data science and AI in predicting toxicity of small molecules
• Import and preprocess molecular data, including handling chemical descriptors and cleaning datasets
• Extract relevant features from molecular datasets to enhance model accuracy
• Develop and evaluate machine learning models, such as classification algorithms, for toxicity prediction
• Visualize model results and interpret performance metrics to assess predictive accuracy
• Utilize MATLAB’s built-in resources and toolboxes to further explore cheminformatics and predictive modeling

Basic familiarity with MATLAB and machine learning concepts is helpful but not required.

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. 

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

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 steps for spatial transcriptomics analysis including QC, exploratory analysis, batch effect removal, integration of spatial and gene expression information, as well as differential expression and pathway analysis.  No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.