Classes & Events

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. 

Part 1 is a  two-hour online training that will address the frequentist approach and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.    

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

• Understand how to use statistical concepts to test hypotheses 

• Interpret the results of statistical tests 

• Make informed decisions about the significance of findings while considering the potential for errors in the analysis 

Attendees are not expected to have any prior knowledge to be successful in this training. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. 

You must register separately for Part 2 of this class series.

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. IT IS STRONGLY RECOMMENDED TO COME IN PERSON.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a several trainings that cover general concepts behind statistics and epidemiology. These trainings will help participants better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature. 

This four-hour online training will provide a brief review of the principles of epidemiology, outbreak investigations, implications in public health, key concepts and terms, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). Time will be devoted to questions from attendees and references will be provided for in-depth self-study. 

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

• Define epidemiology and its key principles 

• Share the purpose and function of outbreak investigations 

• List common statistical methods in epidemiology 

• Describe when to use different statistical tests and measures 

Explain measures of association and confounding 

In this lesson, attendees will continue to learn basic features of the R programming language. The focus of this lesson will be vectors, one of the most common object types in R. You will learn why vectors are useful and how to create, modify, and export vectors.

In this talk, Dr. Stergachis will present data on using Fiber-seq and deaminase-assisted Fiber-seq (DAF-seq) to resolve the functional impact of both germline and somatic genetic variants, as well as to identify somatic epimutations that arise during normal human development and oncogenesis.

In this webinar, the participants will learn about application of machine learning methods, specifically geared towards predicting the toxicity of target molecules. They will gain insights into various machine learning techniques, fostering a comprehensive understanding of their application in this critical domain. Furthermore, attendees will acquire the skills to apply machine learning to their data, utilize applications to train artificial intelligence (AI) models for toxicity prediction, and effortlessly share the results with collaborators.

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

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. 

This one-and-a-half-hour online training will address the Bayesian approach and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.    

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

• Explain the fundamental concepts of Bayesian inference, including Bayes’ Theorem and its applications. 
• Describe the roles of prior and posterior distributions in Bayesian analysis. 
• Interpret the Bayes factor and its use in comparing statistical models. 

Attendees are not expected to have any prior knowledge to be successful in this training. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. 

Part 1 is a pre-requisite for this class. You must register separately for Part 1 of this class series. 

This lesson will introduce data structures including data frames and show attendees how to import data into the R environment. 

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. It is strongly recommended to come in person. 

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.  

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI)-funded initiative to construct 3-dimensional atlases of the dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease. (Cell April 2020).

This tutorial will demonstrate how to perform spatial analysis on HTAN single cell data identifying local cell neighborhoods directly with built in BigQuery functionality.

This webinar is part of a series of Human Tumor Atlas Network (HTAN) presentations. Please see the calendar for other events in this series. 

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

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.