Bioinformatics Training & Education Program

Have you been looking for ways to use artificial intelligence (AI) in clinical practice but not sure where to start? Attend this webinar for tips from Dr. Anant Madabhushi on applying AI in precision oncology. He’ll describe how AI can be affordable, easy to interpret, and help ensure more equitable care for every patient. Specifically, Dr. Madabhushi will discuss efforts by his group to develop AI-based approaches for measuring:

• alterations in the immune architecture underlying diseases (i.e., collagen disorder) using AI and pathology images, and
• changes in tumor blood vessels (vessel tortuosity) using AI and radiologic scans.

He’ll describe how biomarkers like these can help you predict how well a patient will respond to treatment, as well as monitor their response.

Dr. Madabhushi has been on the forefront of translating lab-created technologies into clinical practice. He’s pioneered the use of AI in precision oncology, offering new solutions for diagnosing cancers (i.e., breast, prostate, lung) and predicting how patients will respond to cancer treatments, such as chemotherapy, immunotherapy, and protein inhibitors (i.e., cyclin-dependent kinase 4 and 6).

The Cancer Imaging Program is part of NCI’s Division of Cancer Treatment & Diagnosis. The program hosts this monthly NCI Imaging Community Webinar Series for scientists and clinicians interested in advancing cancer imaging. If you want to learn about upcoming opportunities for engagement, interdisciplinary collaboration, and current research showcases, you can explore upcoming events on the webinar series’ webpage.

Dr. Anant Madabhushi is the Robert W. Woodruff Professor of Biomedical Engineering and serves on the faculty of the departments of pathology, biomedical informatics, urology, radiation oncology, radiology and imaging sciences, global health and computer and information sciences, all at Emory University. He’s also a research career scientist at the Atlanta Veterans Administration Medical Center. And he’s director of the Emory Empathetic AI for Health Institute at Emory University. Dr. Madabhushi has authored more than 500 peer-reviewed scientific publications and holds (or has pending) over 200 patents in the areas of AI, radiomics, medical image analysis, computer-aided diagnosis, and computer vision.

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read, easier to edit, less prone to errors,  and often allows it to run more efficiently. This intermediate class will provide an overview of what is a macro and how macros work in SAS, including the macro facility. Examples of macro code will be made available to class participants for modification and later use.

Galaxy is a scientific workflow, data integration, data analysis, and publishing platform that makes computational biology accessible to research scientists that do not have computer programming experience. This workshop will introduce RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The hands-on exercises will run on the Galaxy platform using Illumina paired-end RNA-seq data. The workshop will be taught by NCI staff and is open to NIH and HHS staff.

Presented as part of the Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) Webinar Series

Dr. Philip Lupo is a professor of pediatrics and hematology-oncology at Baylor College of Medicine. His lab focuses on the molecular epidemiology of pediatric disease and conditions. His areas of interest include understanding the risk of cancer among children with structural birth defects, determining inherited genes underlying susceptibility to rhabdomyosarcoma, phenomic and genomic studies of structural birth defects, and addressing disparities in acute lymphoblastic leukemia susceptibility and outcomes. In this webinar, Dr. Lupo will be presenting on leveraging population-based registries for genomic studies of pediatric cancer.

For more information, contact Leah Mechanic.

If you’re a researcher, clinician, informaticist, commercial partner, or policy maker interested in cancer data science, register to attend this Cancer Data Exchange Summit.

You’ll have the opportunity to hear (and take part in) discussions around current opportunities and challenges with the following topics:

• How to use a patient’s data to determine their eligibility for clinical trials
• How to identify and develop data standards to detect immune-related adverse events
• Ways to enhance the efficiency and timeliness of the collection of cancer registry data
• Ways to support patient access, interoperability, and data sharing

You can also help identify cancer-specific elements; develop implementation guides; and define requirements to build large language models for extracting data.

The participant group will comprise researchers, clinicians, informatics/data scientists, patient advocates, standard-setting organizations (such as HL7/FHIR), policymakers, EHR vendors, and industry partners. Their collaborative efforts will focus on identifying current opportunities, challenges, and essential oncology-specific data requirements for the USCDI+ Cancer use cases (1) using real-world data to determine patient eligibility for clinical trials; (2) identifying immune-related adverse events; (3) enhancing the efficiency and timeliness of cancer registry data.

Qlucore Omics Explorer is a point-and-click software that enables analysis of RNA sequencing (bulk and single cell), proteomics and metabolomics data. It’s machine learning capabilities also allow for classification of cell types. This software is available to NCI CCR scientists. In this session, participants will learn to analyze bulk RNA sequencing data using Qlucore Omics Explorer. Topics discussed include experimental design, data import, normalization, differential expression analysis, and biological interpretation in and outside Qlucore (i.e. GSEA, pathway visualization, biological networks, GO enrichment). Experience using this software is not required to attend. Participants are encouraged to install Qlucore Omics Explorer by submitting a ticket with the NCI computer service desk (service.cancer.gov) prior to the event.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=mbcf05ad560604862467c52417b2c399b
Meeting number:
2303 382 3263
Password:
NTmpQhY@733

Join by video system
Dial 23033823263@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
1-650-479-3207 Call-in number (US/Canada)
Access code: 2303 382 3263

This one-hour session will introduce attendees to the world of Artificial Intelligence (AI) as we explore the fundamentals, applications, and ethical considerations of this transformative technology. Key topics will include machine learning, deep learning, data handling, and real-world AI applications across various industries. We'll delve into the ethical implications of AI and offer insights on how to become 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.

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of R programming language and RStudio. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; organize files and directories for a set of analyses as an R Project; define the following terms as they relate to R: object, assign, comment, call, function, and arguments; and assign values to objects in R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Please bring your laptop with R and RStudio installed.

Please join us for the May Data Sharing and Reuse Seminar featuring Dr. Ali Loveys and Fiona Meng from FI Consulting. They will be sharing their presentation on Laying the Foundation for AI-Ready Data. In September 2023, the NIDDK Central Repository announced a challenge to enhance NIDDK data sets for future AI applications. Participants utilized data from longitudinal studies on type 1 diabetes (TEDDY and TrialNet). FI Consulting's team, led by Dr. Ali Loveys, successfully consolidated and unified TrialNet data sets, identified data outliers, and ensured consistent variable representation. Their efforts created a data set for time-series analysis, making it more likely to inform prevention and personalized treatment plans for those at risk of diabetes and diabetes-related complications.

This seminar is open to the public and registration is required each month. Individuals who need interpretating services and/or other reasonable accommodations to participate in this event should contact Janiya Peters (jpeters@scgcorp.com) at 301-670-4990. Request should be made at least three business days om advance of the event. A recording will be available on this page after each event.

This presentation will explain the difference between the mean and standard deviation of a set of values and the standard error of the mean. The parameters involved in comparing two normally distributed populations relative to a single value are the sample size, the effect size, the standard deviations of the distributions, the significance level, and the power. We will discuss the relationship between these parameters and accuracy, and how increasing the sample size will, in general, not change the effect size or the standard deviations of the populations, but will increase the significance (i.e. decrease the p-value) of the effect size. This will be a hybrid event. This session will be recorded, and materials will be posted on the ABCS training site and also shared with attendees a few days after the event.

For additional details and questions, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science group, Frederick National Laboratory for Cancer Research.

Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models.

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

Join Dr. Karchin of the Johns Hopkins School of Medicine and Dr. Krieg of the Medical University of South Carolina as they discuss the novel use of artificial intelligence (AI) in immunotherapy target discovery.

Attend this webinar to learn how:

• AI advances could quickly improve clinical care.
• you can use AI to better analyze large-scale data sets for biomarkers that can enhance immunotherapy research.

This webinar is part of the 2024 SITC-NCI Computational Immuno-oncology Webinar Series, which focuses on the application of AI in immuno-oncology. This is the second of nine free webinars to help individual research labs overcome computational challenges while analyzing and integrating different assay data throughout the immuno-oncology spectrum using AI. The annual series aims to educate early-career scientists, increase participants’ awareness of and engagement in NCI-supported Cancer Moonshot℠ Immunotherapy Networks, and fulfill the Blue Ribbon Panel’s goal of accelerating progress in cancer research.

Generalist repositories offer NIH researchers a flexible, trusted resource to share data for which there is no appropriate discipline specific repository as well as to share many other research outputs valuable for reproducibility and open science. This webinar, presented by participants of the NIH Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo) will share generalist repository use cases and best practices for sharing and finding data in generalist repositories. It will describe how generalist repositories fit into the NIH data repository landscape for intramural researchers and can be part of meeting the new NIH Data Management and Sharing Policy requirements. It will present both the key common features of generalist repositories that meet the NIH desirable repository characteristics as well as the unique features of these repositories that make them suited to specific types of data. 

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. Part 1 of this training will cover understanding research data, how to manage research data, and how to work with data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 2 of this class.

Qiagen CLC Genomics Workbench is a point-and-click bioinformatics software that runs on a personal computer and enables bulk RNA sequencing, ChIP sequencing, long reads, and variant analysis. This software is available to NCI scientists.

This hands-on training will guide participants through bulk RNA sequencing analysis using CLC Genomics Workbench. After the class, participants will be able to

• Import files and illumina reads
• Import and associate metadata with samples
• Download reference genome and annotation
• Obtain RNA sequencing expression counts and perform differential expression analysis
• Construct PCA and heatmap to visualize RNA sequencing data

To get the most of this hands-on session, please reach out to the NCI service desk (https://service.cancer.gov/ncisp) to get this software installed, preview the tutorial (https://resources.qiagenbioinformatics.com/tutorials/RNASeq-droso.pdf), and download the example dataset (http://resources.qiagenbioinformatics.com/testdata/RNA_Seq_Droso2.zip) prior to attending.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m07f826d16b67d3c3b8a86e275ebac5a5
Meeting number:
2300 281 6121
Password:
e7aEqhpy@34

Join by video system
Dial 23002816121@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
1-650-479-3207 Call-in number (US/Canada)
Access code: 2300 281 6121

The NIH Artificial Intelligence (AI) Symposium will take place on Friday, May 17th, 2024, in Masur Auditorium in Building 10 on the Bethesda NIH campus. This event is open to all NIH members - registration and abstract submission are now open https://forms.microsoft.com/g/4WpdBXcEu6

Biomedical science is in a technological revolution, driven by innovations in deep learning architecture and computational power. These cutting-edge techniques are being applied to every sub-field of the biological sciences, and with ground-breaking advancements arriving constantly it is challenging for researchers to stay up to speed on what is possible. This one-day 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.

Keynote speakers James Zou, Ph.D. (Stanford University) and Hari Shroff, Ph.D. (Janelia Research Campus) will share their research, and also participate in a Panel Discussion on the current and future potential of AI in biomedical sciences. There will also be short talks and posters from researchers on campus who are developing or using AI approaches.

The NIH AI Symposium is sponsored by NHLBI, in partnership with FAES. Registration and abstract submission are open to all NIH members, including experts in AI-related fields and novices interested in gaining more exposure.

Important dates:

March 15th - Abstract submission deadline

April 5th - Abstract notifications

May 3rd – Registration deadline

Sign language interpreting and CART services are available upon request to participate in this event. Individuals needing either of these services and/or other reasonable accommodations should contact Ryan O’Neill (oneillrs@nih.gov).

Questions can be directed to Lead Organizer Ryan O’Neill, Ph.D. (oneillrs@nih.gov).

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. During Part 2 of this training, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 1 of this class.