Bioinformatics Training & Education Program

This is the last lesson in Part 1 of Introductory R for Novices: Getting Started with R. This lesson will focus exclusively on working with data frames. Attendees will learn how to examine, summarize, and access data in data frames.  

Join Dr. Eytan Ruppin, NCI investigator in the Center for Cancer Research, as he discusses Path2Space, a new and unpublished deep learning approach that predicts spatial gene expression directly from histopathology slides.

Spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by providing high-resolution, location-specific mapping of gene expression within tumors and their microenvironment. However, high costs have restricted the size of cohorts, limiting large-scale biomarker discovery.

With Path2Space, you can:

• predict the spatial expression of over 4,300 breast cancer genes in independent validations, thereby outperforming existing ST predictors.
• accurately infer cell-type abundances in the tumor microenvironment (TME).
• apply to over 1,000 breast tumor histopathology slides from the TCGA, characterizing their TME on an unprecedented scale, and identify new spatially grounded breast cancer subgroups with distinct survival rates.
• infer TME landscapes, enabling more accurate predictions of patients’ response to chemotherapy and trastuzumab.
• operate a transformative, fast, and cost-effective approach to robustly delineate the TME.

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. 

Attend this virtual seminar via Teams. The Division of Intramural Research (DIR) sponsors a monthly series of talks by intramural and special guest speakers celebrating genetics and genomics research. Speakers are selected by NHGRI intramural faculty and trainees and cover research topics of interest to a wide audience.

In this one hour and half hour online training, attendees will apply deep learning to brain MRI images.  

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

• Recognize multiple methods of generating models 
• Interrogate the models with explainability techniques, such as applying artificial intelligence (AI) to data, using apps to train AI models for prediction, and sharing results with collaborators. 

 This is an introductory-level training taught by MathWorks. No installation of MATLAB is necessary.  

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

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.

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 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. 

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 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.