Classes & Events

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. 

On October 15th-16th, 2024, the NCI Office of Data Sharing (ODS) is hosting the Annual Data Sharing Symposium titled Driving Cancer Advances through Impactful Research inside the Clinical Center at the National Institutes of Health in Bethesda, MD.

Register for the symposium and pass along to your network!

A foundational shift to a culture of broad data sharing and collaborative science holds immense promise for more rapid advances in cancer research. NCI’s Office of Data Sharing will bring together experts and stakeholders including scientists, clinicians, policymakers, patients, advocates and trainees across government, academia and industry to learn from one another and explore ways to maximize the benefits of these efforts. Please join us for engaging discussions on advancing cancer research through data sharing and reuse. The Symposium will include sessions on key topics of interest: 

• Honoring the Contributions of Research Participants 
• Highlighting the Broad Impact of Data Sharing and Reuse 
• Exploring a Learning Health System for Cancer
• Review and Look Ahead to the impact of AI on Data Sharing
• Improving Data Access and Utility 

This two-day event will include learning sessions, panel discussion and thinktank opportunities to address meaningful data sharing and data use and will be followed immediately by the NCI Cancer Research Data Commons (CRDC) Symposium which will focus on how this data science infrastructure fits in the data sharing lifecycle to support cancer research.

The ultimate goals of improved data sharing are to enhance the abilities of the cancer research and care community to learn from every patient to achieve better prevention, treatment, and outcomes for all who are affected by cancer.

Email the Office of Data Sharing if you would like more information or have general questions.

This one-hour online training will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. This training will also discuss the differences between animal models, research organisms, and model organisms, and will review requirements and resources for the NIH Model Organism Sharing Policy.  

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

• Describe the difference between animal models, research organisms, and model organisms 

• Identify requirements for the NIH Model Organism Sharing Policy 

• Locate biomedical articles and patents related to animal models 

• Discover NIH-funded research projects, genetic information, and biomedical literature related to specific research organisms 

• Explore books on animal models and model organisms 

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

Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).

 Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions at NIH, where you’ll get hands-on, step-by-step training using sample datasets (.csv, .tsv, .txt) with MAWA (Multiplex Analysis Web Apps). This user-friendly, graphical software platform offers an end-to-end solution for your analysis workflow following cell segmentation of your tissue.

 What You’ll Learn:

• File Handling: Efficiently manage your datasets.
• Phenotyping: Identify and categorize cell types.
• Spatial Analysis: Analyze spatial relationships within your data.

Why Attend?

• Easy-To-Use Platform: MAWA offers an intuitive interface suitable for both novices and experts.
• High Performance: Efficiently handles large datasets with millions of cells or objects.
• Free Access: MAWA is available for free use.

 Don’t miss this opportunity to enhance your data analysis skills with MAWA!

 Planned schedule:

 Tue 10/15, 1-2 PM

Topic: Introduction to NIDAP, MAWA Basics, and Supervised Phenotyping

Speaker: Andrew Weisman, Ph.D.

Tue 10/22, 1-2 PM

Topic: Unsupervised phenotyping.

Speaker: Andrei Bombin, Ph.D.

Tue 10/29, 1-2 PM

Topic: Pairwise spatial analysis using hypothesis testing.

Speaker: Andrew Weisman, Ph.D.

Wed 11/6, 11-12 PM

Topic: Neighborhood analysis using spatial UMAP.

Speaker: Dante Smith, Ph.D.

The CRDC will celebrate its 10th anniversary with this one-and-a-half-day event highlighting its accomplishments and looking ahead to exciting initiatives. We are planning many informative sessions and report-outs on new work, including our AI Readiness Initiative and the CRDC’s collaboration with the Advanced Research Projects Agency for Health (ARPA-H) to develop a Biomedical Data Fabric (BDF) Toolbox.
 
Our Fall Symposium shares a half-day joint session, focused on data sharing, with CBIIT’s Office of Data Sharing (ODS) Annual Meeting. The joint session, on the afternoon of October 16th, will be the final session of the ODS Meeting, and the first session of the CRDC Symposium.  If you can make it to all three days, so much the better!
 
The ODS Annual Meeting runs October 15-16. The registration page is here: https://events.cancer.gov/ods/annualdatasharingsymposium 

An event registration page and preliminary agenda are available here: https://events.cancer.gov/crdc/events
 
The CRDC has come a long way in the last 10 years as we have empowered the cancer research community with access to NCI-funded research data, secure cloud-based workspaces, analytical tools, and an evolving infrastructure to address the rapidly changing research landscape. We hope that you will engage with us – in person or virtually – as we all look ahead to the next 10 years.  

Dear Colleagues,
 
Thanks to advances in single-cell genomics, researchers can construct large-scale organ atlases, giving you more accurate ways to study genetic mutations and alterations related to drug responses and disease. These models also create a unique opportunity to use artificial intelligence (AI) to better understand cellular responses, using both multiomic and spatial data.
 
In this upcoming webinar, hear Dr. Fabian Theis discuss how AI is enabling researchers to model single-cell variation, potentially creating a single-cell foundation model.
 
Dr. Theis will:
•    review deep learning approaches for identifying gene expression,
•    outline applications for cell atlas building,
•    address concerns (such as variations in drug responses and multiscale readouts),
•    explain organism-wide cell type predictors, and
•    review the future of foundation models and their potential impact on spatial omics for modeling the cellular niche.

Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact Laurie Morrissey (240-276-5154, laurie.morrissey@nih.gov), and/or the Federal TTY Relay number (1-800-877-8339). Requests should be made at least five days in advance of the event.

In this webinar, attendees will learn to call MATLAB from Python and to call Python libraries from MATLAB.  In addition, they will learn how to use MATLAB’s Python integration to improve the compatibility and usability of the code. 

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

Popular structure prediction program AlphaFold3 and its competitor Chai-1 recently added capabilities to predict 3D RNA structures straight from sequence input. In this talk, we will discuss some test cases for these programs and review useful legacy tools that split the RNA structure prediction problem into 2D and 3D steps. Select FRCE web servers and other software encapsulated in virtual machines available for download from FRCE will be presented. No prior knowledge of RNA prediction programs or servers is assumed. A familiarity with Unix shell interface is helpful. This session will be recorded, and materials will be posted on the ABCS training site and will also be shared with attendees a few days after the event. For details, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science (ABCS) group, Frederick National Laboratory for 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. 

Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).

 Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions at NIH, where you’ll get hands-on, step-by-step training using sample datasets (.csv, .tsv, .txt) with MAWA (Multiplex Analysis Web Apps). This user-friendly, graphical software platform offers an end-to-end solution for your analysis workflow following cell segmentation of your tissue.

 What You’ll Learn:

• File Handling: Efficiently manage your datasets.
• Phenotyping: Identify and categorize cell types.
• Spatial Analysis: Analyze spatial relationships within your data.

 Why Attend?

• Easy-To-Use Platform: MAWA offers an intuitive interface suitable for both novices and experts.
• High Performance: Efficiently handles large datasets with millions of cells or objects.
• Free Access: MAWA is available for free use.

 Don’t miss this opportunity to enhance your data analysis skills with MAWA!

 Planned schedule:

 Tue 10/15, 1-2 PM

Topic: Introduction to NIDAP. MAWA Basics, and Supervised Phenotyping.

Speaker: Andrew Weisman, Ph.D.

Tue 10/22, 1-2 PM

Topic: Unsupervised phenotyping.

Speaker: Andrei Bombin, Ph.D.

Tue 10/29, 1-2 PM

Topic: Pairwise spatial analysis using hypothesis testing.

Speaker: Andrew Weisman, Ph.D.

Wed 11/6, 11-12 PM

Topic: Neighborhood analysis using spatial UMAP.

Speaker: Dante Smith, Ph.D.

CellMinerCDB is an interactive public web application (https://discover.nci.nih.gov/cellminercdb/) that simplifies access and exploration of cancer cell line pharmacogenomic data across different sources such as the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MAACC). It leverages overlaps of cell lines and drugs across databases to examine reproducibility, expand association and pathway analyses, and discover drug response biomarkers.

Additional Meeting Information

Meeting number:

2308 686 9253

Password:

KWihcpc$588

Join by video system

Dial 23086869253@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: 2308 686 9253

This one-hour online training will cover tips and tricks to run your processing against large datasets more efficiently in SAS.  

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

• Describe general best coding practices that, when processing large data, will speed up performance 

• Discuss best practices in using PROC SQL and DATA Step in working with large data 

• Identify best practices in processing against external data sources 

Attendees are expected to be familiar with the basic functions of SAS to be successful in this training. Contact nihlibrary@nih.gov for information on how to access on-demand introductory SAS trainings. 

Recent advances in artificial intelligence (AI) have revolutionized the use of hematoxylin and eosin (H&E)-stained tumor slides for precision oncology, enabling data-driven approaches to predict molecular characteristics and therapeutic outcomes. In my talk, I will present ENLIGHT–DeepPT, a novel two-step AI framework. The first step, DeepPT, leverages deep learning to predict genome-wide tumor mRNA expression from H&E slides. The second step, ENLIGHT, utilizes these inferred expression values to predict patient response to targeted and immune therapies. We validate this framework across 16 cohorts from The Cancer Genome Atlas (TCGA) and independent datasets, demonstrating successful prediction of true responders in five patient cohorts spanning six cancer types, with a 39.5% increased response rate and an odds ratio of 2.28.

In addition, I will introduce DEPLOY, a deep learning model designed to enhance the diagnosis of central nervous system (CNS) tumors by predicting tumor categories from histopathology slides. DEPLOY integrates three components: a direct classifier based on histopathology images, an indirect model that predicts DNA methylation profiles for tumor classification, and a model that uses patient demographics. Trained on a dataset of 1,796 patients and tested on independent cohorts of 2,156 patients, DEPLOY achieves 95% overall accuracy and 91% balanced accuracy. These results underscore the potential of DEPLOY to assist pathologists in classifying CNS tumors rapidly, offering a promising tool for improving diagnostic precision in clinical settings.

Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).

 Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions at NIH, where you’ll get hands-on, step-by-step training using sample datasets (.csv, .tsv, .txt) with MAWA (Multiplex Analysis Web Apps). This user-friendly, graphical software platform offers an end-to-end solution for your analysis workflow following cell segmentation of your tissue.

 What You’ll Learn:

• File Handling: Efficiently manage your datasets.
• Phenotyping: Identify and categorize cell types.
• Spatial Analysis: Analyze spatial relationships within your data.

Why Attend?

• Easy-To-Use Platform: MAWA offers an intuitive interface suitable for both novices and experts.
• High Performance: Efficiently handles large datasets with millions of cells or objects.
• Free Access: MAWA is available for free use.

Don’t miss this opportunity to enhance your data analysis skills with MAWA!

Planned schedule:

Tue 10/15, 1-2 PM

Topic: Introduction to NIDAP. MAWA Basics and Supervised Phenotyping.

Speaker: Andrew Weisman, Ph.D.

Tue 10/22, 1-2 PM

Topic: Unsupervised phenotyping.

Speaker: Andrei Bombin, Ph.D.

Tue 10/29, 1-2 PM

Topic: Pairwise spatial analysis using hypothesis testing.

Speaker: Andrew Weisman, Ph.D.

Wed 11/6, 11-12 PM

Topic: Neighborhood analysis using spatial UMAP.

Speaker: Dante Smith, Ph.D.

In this session of the BTEP Coding Club, Emily Clough, PhD, GEO Curator, will explore updates to analysis tools available within the Gene Expression Omnibus (GEO), a public repository for gene expression and epigenomics data sets. In the past several years GEO has made major updates and additions to the online analysis tool GEO2R. Many new visualization plots have been added to explore results, and now human RNA-seq data are available for analysis.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m0b499a1174cc32a9355c87c395b5ac15

Meeting number:
2318 999 6974

Meeting password:
dVuQdig?937

Join from a video or application
Dial 23189996974@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
1-650-479-3207 Toll
Access code: 23189996974

Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).

 Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions at NIH, where you’ll get hands-on, step-by-step training using sample datasets (.csv, .tsv, .txt) with MAWA (Multiplex Analysis Web Apps). This user-friendly, graphical software platform offers an end-to-end solution for your analysis workflow following cell segmentation of your tissue.

What You’ll Learn:

• File Handling: Efficiently manage your datasets.
• Phenotyping: Identify and categorize cell types.
• Spatial Analysis: Analyze spatial relationships within your data.

Why Attend?

• Easy-To-Use Platform: MAWA offers an intuitive interface suitable for both novices and experts.
• High Performance: Efficiently handles large datasets with millions of cells or objects.
• Free Access: MAWA is available for free use.

Don’t miss this opportunity to enhance your data analysis skills with MAWA!

 Planned schedule:

 Tue 10/15, 1-2 PM

Topic: Introduction to NIDAP. MAWA Basics, and Supervised Phenotyping.

Speaker: Andrew Weisman, Ph.D.

Tue 10/22, 1-2 PM

Topic: Unsupervised phenotyping.

Speaker: Andrei Bombin, Ph.D.

Tue 10/29, 1-2 PM

Topic: Pairwise spatial analysis using hypothesis testing.

Speaker: Andrew Weisman, Ph.D.

Wed 11/6, 11-12 PM

Topic: Neighborhood analysis using spatial UMAP.

Speaker: Dante Smith, Ph.D.

Dr. Blackshaw's work examines the molecular basis of neuronal and glial cell fate specification and survival, focusing on characterizing the network of genes that control specification of different cell types within the retina and hypothalamus, two structures that arise from the embryonic forebrain.  The ultimate goal is to use insights gained from learning how individual cell types are specified to understand how these cells contribute to the regulation of behavior, and how they can be replaced in neurodegenerative disease.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature.

Part 1 will address fundamental statistical concepts including hypothesis testing, p-values and confidence intervals, types of data and their distributional importance, and bias and confounding. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.

Although you may attend any part of this series by itself, attending all four parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other. You must register separately for each part of this class series.

In this hour and half online training, attendees will learn how to improve and optimize their MATLAB code to boost execution speed by orders of magnitude. The training covers common pitfalls in writing MATLAB code, explores the use of the MATLAB Profiler to find bottlenecks, and introduces the use of Parallel Computing Toolbox. The training also addresses vectorization and best coding practices in MATLAB. 

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

• Incorporate compiled languages, such as C, into MATLAB applications 

• Utilize additional hardware, such as multicore processors and GPUS to improve performance 

• Scale up to a computer cluster, grid environment or cloud 

This training is for beginners through experienced; no software installation 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. 

This webinar will provide an overview of the generative artificial intelligence (AI) services across three major cloud service providers (CSPs) and the steps to follow for setting up AI chatbots in cloud environments with a bioinformatic focus. Following this will be a demonstration on workflow managers for bioinformatic analysis including Snakemake, Workflow Description Language (WDL), and Nextflow. Brought to you be the STRIDES initiative at NIH. 

This one-hour online training will cover several easy-to-use tools for analytic situations, including PROC FREQ (chi-square tests, Fisher's exact test), PROC TTEST, and PROC NPAR1WAY. This training covers the basic guidelines for using different tests with examples. 

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

• Review data with a listing report 

• Explore categorical variables using Proc Freq 

• Explore continuous variables using Proc Means and Proc Univariate 

• Review hypothesis testing 

• Perform association analysis with categorical and ordinal variables using Proc Freq 

• Perform analysis of variance with continuous variables using Proc t-test 

• Perform nonparametric analysis using Proc Npar1way 

• Identify resources for learning more 

Attendees are expected to be familiar with the basic functions of SAS to be successful in this training. Contact nihlibrary@nih.gov for information on how to access on-demand introductory SAS trainings. 

The accessibility of artificial intelligence/machine learning (AI/ML) tools has taken off in recent years. This democratization of advanced analytics has the potential to revolutionize predictive toxicology, especially for applications that generate massive, multimodal data. Realizing this promise will require tools tuned to learn from trusted sources that can evolve as new data emerge. This talk will describe such efforts at NIEHS using data that range in scale from lab-based behavioral experiments to epidemiological-scale geospatial data.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature. 

Part 2 will provide a review of study designs in biomedical research. This class will cover details related to case studies/series, ecological, cross-sectional, case-control, and cohort studies, clinical trials, and other study designs and considerations. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.

Although you may attend any part of this series by itself, attending all four parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other.

You must register separately for each part of this class series.

This one hour and a half online training in the NIH Library Evidence Synthesis Review series provides an overview of the data collection process for your review. The training will cover how to clean the data and the importance of this step to ensuring accurate data is collected from each included article. 

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

• Describe an overview of the data collection process 

• Name 2 tools used for data collection 

• Identify the number of people needed to collect the data 

• Explain the importance of defining your variables to collect 

• Understand the importance of piloting the data collection process 

Attendees are not expected to have prior knowledge of how to conduct a review. It is recommended that those planning to undertake a review, should register for the Evidence Synthesis series that take a deeper dive into the required methods for each step in a review.   NIH

The primary theme of Dr. Bult's personal research program is “bridging the digital biology divide,” reflecting the critical role that informatics and computational biology play in modern biomedical research. Dr. Bult is a Principal Investigator in the Mouse Genome Informatics (MGI) consortium that develops knowledge-bases to advance the laboratory mouse as a model system for research into the genetic and genomic basis of human biology and disease. Recent research initiatives in Dr. Bult's research group include computational prediction of gene function in the mouse and the use of the mouse to understand genetic pathways in normal lung development and disease.