Supported by CCR Office of Science and Technology Resources (OSTR)
ncibtep@nih.gov

Bioinformatics Training and Education Program

Featured

Upcoming Classes & Events

May

Organized by
NIAID
Description

The symposium's goals are to explore the integration of AI in understanding and managing immunological data, foster a paradigm shift in how immunologists leverage AI to propel their research forward, and inform NIAID about existing AI resources, needs, and future directions to support DAIT. The symposium will:

  • Present stimulating use cases covering AI for immunology, e.g., concrete examples where AI has already made significant contributions Read More

The symposium's goals are to explore the integration of AI in understanding and managing immunological data, foster a paradigm shift in how immunologists leverage AI to propel their research forward, and inform NIAID about existing AI resources, needs, and future directions to support DAIT. The symposium will:

  • Present stimulating use cases covering AI for immunology, e.g., concrete examples where AI has already made significant contributions to immunology
  • Identify near-term and long-term challenges and barriers, e.g., address current limitations and challenges facing the integration of AI in immunology
  • Discuss the scientific and clinical opportunities empowered by the AI revolution, e.g., how it could revolutionize our understanding of the immune system, lead to groundbreaking treatments, and influence public health policy. 

This is a hybrid meeting where attendees can choose to attend in-person or via Zoom Government.

Speakers and Moderators who are part of this program are expected to attend in person.

In-person registration is required by Tuesday, May 21, 2024

https://web.cvent.com/event/b1808ba5-fb93-4bf9-a253-dc63938869a9/summary

For programmatic questions, please contact dait_ai_workshop@mail.nih.gov.

For meeting logistical questions, please contact Heather Leonard, Lumina Corps, at EventsNIAID@luminacorps.com.

Organized by
NCI
Description

NCI is launching the virtual Cancer AI Conversations series featuring multiple perspectives on timely topics and themes in artificial intelligence for cancer research! 

Each event features short talks from 2-4 subject matter experts offering diverse views on the session topic. These talks will be followed by a moderated panel discussion.

“Cancer AI Conversations” are bimonthly, 1-hour virtual events featuring timely topics related to the application Read More

NCI is launching the virtual Cancer AI Conversations series featuring multiple perspectives on timely topics and themes in artificial intelligence for cancer research! 

Each event features short talks from 2-4 subject matter experts offering diverse views on the session topic. These talks will be followed by a moderated panel discussion.

“Cancer AI Conversations” are bimonthly, 1-hour virtual events featuring timely topics related to the application of artificial intelligence in cancer research. Each event features short talks from 2-4 subject matter experts offering diverse perspectives on the session topic. 

All of the Cancer AI Conversations will be recorded and posted for future viewing.

Organized by
NCI
Description

Frederick Research Computing Environment (FRCE) and Computational Sciences Series

In this session, we will explore how machine learning can be used to analyze whole slide pathological images (WSIs). We will showcase how to utilize the Frederick Research Computing Environment (FRCE) to speed up processing using GPUs. Having a foundational understanding of machine learning or deep learning could be advantageous.


This session will be recorded, and materials will be posted Read More

Frederick Research Computing Environment (FRCE) and Computational Sciences Series

In this session, we will explore how machine learning can be used to analyze whole slide pathological images (WSIs). We will showcase how to utilize the Frederick Research Computing Environment (FRCE) to speed up processing using GPUs. Having a foundational understanding of machine learning or deep learning could be advantageous.


This session will be recorded, and materials will be posted on the Advanced Biomedical Computational Science training site and will also be shared with attendees a few days after the event. For details, please contact Natasha Pacheco, of the Advanced Biomedical Computational Science (ABCS) group at Frederick National Laboratory for Cancer Research.


If you are an individual with a disability who needs reasonable accommodations to participate in this event, please contact Natasha Pacheco at least five business days before the event, so that we can discuss your accommodation request.

 

Organized by
NIH Library
Description

This class provides a basic overview of creating plots using ggplot. ggplot is a part of the Tidyverse, a collection of R packages designed for data science. This class will focus on identifying the appropriate packages for plotting, defining plot aesthetics, and demonstrating how to add layers to ggplot graphs.  You must Read More

This class provides a basic overview of creating plots using ggplot. ggplot is a part of the Tidyverse, a collection of R packages designed for data science. This class will focus on identifying the appropriate packages for plotting, defining plot aesthetics, and demonstrating how to add layers to ggplot graphs.  You must have taken Introduction to R and RStudio class to be successful in this class. 

By the end of this class, participants should be able to discuss the connection between data, aesthetics, & the grammar of graphics, describe how ggplot works, define geoms, and distinguish between individual geoms and collective geoms.

Organized by
NIH Library
Description

This class provides an overview of options for customizing a ggplot graph. This class will focus on methods for creating small multiples, options for customizing a graph, and how to apply ggplot themes. You must have taken Data Visualization in R: ggplot class to be successful in this class.

By the end of this class, participants should be able to describe options for time series data, create a line Read More

This class provides an overview of options for customizing a ggplot graph. This class will focus on methods for creating small multiples, options for customizing a graph, and how to apply ggplot themes. You must have taken Data Visualization in R: ggplot class to be successful in this class.

By the end of this class, participants should be able to describe options for time series data, create a line plot in ggplot, learn how to facet a plot, demonstrate options for customizing the title and axis, and apply different ggplot themes.

Organized by
CBIIT
Description

Dear Colleagues,
 
As machine learning permeates across biomedical research, achieving optimal accuracy demands more than just model deployment. 
Join us for a webinar where we explore post-processing techniques designed to elevate the accuracy and efficiency of prediction models. Using interactive tools in MATLAB, we will evaluate machine learning models, refine predictions, and discuss how to apply these techniques to your work.

Key Takeaways:

• Gain Read More

Dear Colleagues,
 
As machine learning permeates across biomedical research, achieving optimal accuracy demands more than just model deployment. 
Join us for a webinar where we explore post-processing techniques designed to elevate the accuracy and efficiency of prediction models. Using interactive tools in MATLAB, we will evaluate machine learning models, refine predictions, and discuss how to apply these techniques to your work.

Key Takeaways:

• Gain a deeper understanding of the benefits of post-processing in optimizing your work
• Implement post-processing techniques to refine and enhance predictions
• Use interactive tools to streamline workflows and reduce manual coding time

For questions, contact Daoud Meerzaman or Kayla Strauss.

Getting Started with scRNA-Seq Seminar Series

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Organized by
BTEP
Description

This talk will cover a scRNA-seq workflow available to NCI researchers on NIDAP. NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform that hosts user-friendly bioinformatics workflows. This platform allows researchers to use many of the open-source tools discussed in this seminar series without necessitating coding experience. This seminar will focus on the capabilities of this workflow for QC, annotation, and visualization of single-cell Read More

This talk will cover a scRNA-seq workflow available to NCI researchers on NIDAP. NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform that hosts user-friendly bioinformatics workflows. This platform allows researchers to use many of the open-source tools discussed in this seminar series without necessitating coding experience. This seminar will focus on the capabilities of this workflow for QC, annotation, and visualization of single-cell RNA-seq data.

Organized by
NIH Library
Description

This 90-minute course equips 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 course features live demonstrations and group discussion, and provides valuable skills for the effective use of LLMs. Attendees are encouraged to 

This 90-minute course equips 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 course features live demonstrations and group discussion, and provides valuable skills for the effective use of LLMs. Attendees are encouraged to register for a free ChatGPT account prior to taking this class. 

June

Organized by
NIH Library
Description

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 training will introduce ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow.  This workshop will be taught by NCI staff and is open to NIH and HHS staff.

This class is a mixture of lecture and hands-on exercises. By the Read More

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 training will introduce ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow.  This workshop will be taught by NCI staff and is open to NIH and HHS staff.

This class is a mixture of lecture and hands-on exercises. By the end of this class, students will be able to: independently run basic ChIP-seq analysis for peak calling, run quality control on ChIP-seq data, map raw reads to a reference genome, generate alignment statistics and check mapping quality, call peaks using MACS, annotate peaks, and visualize the enriched regions.

Join Meeting
Organized by
CCR Genomics Core
Description

The CCR Genomics Core Facility is pleased to host a virtual technology workshop with EpiCypher on CUT&RUN library prep/sequencing

Presentation overview: The location of histone post-translational modifications and chromatin-associated proteins within the genome is an important consideration for many cancer researchers. Today, there are many assays available to map how this landscape shifts in disease states or in response to treatment—but which is the best one for Read More

The CCR Genomics Core Facility is pleased to host a virtual technology workshop with EpiCypher on CUT&RUN library prep/sequencing

Presentation overview: The location of histone post-translational modifications and chromatin-associated proteins within the genome is an important consideration for many cancer researchers. Today, there are many assays available to map how this landscape shifts in disease states or in response to treatment—but which is the best one for your lab? In this seminar, we will explore CUT&RUN, a revolutionary epigenomic mapping tool that is quickly replacing ChIP-Seq for understanding the role of the epigenome in cancer research. Whether you’re a current CUT&RUN researcher looking to improve your experimental outcomes, a ChIP-Seq expert interested in new technologies, or a new user curious about how CUT&RUN can be used to profile your favorite epigenetic targets, this webinar will set you on the path to success!

For questions about this seminar please Liz Conner, CCR Genomics Core

Distinguished Speakers Seminar Series

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Organized by
BTEP
Description

The Brooks Lab developed a computational tool called FLAIR (Full-Length Alternative Isoform Analysis of RNA) to produce confident transcript isoforms from long-read RNA-seq data with the aim of alternative isoform detection and quantification. With an increase in the usage of long-read RNA-seq, there is a growing need for a systematic evaluation of this approach. We are part of an international community effort called the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) to perform such Read More

The Brooks Lab developed a computational tool called FLAIR (Full-Length Alternative Isoform Analysis of RNA) to produce confident transcript isoforms from long-read RNA-seq data with the aim of alternative isoform detection and quantification. With an increase in the usage of long-read RNA-seq, there is a growing need for a systematic evaluation of this approach. We are part of an international community effort called the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) to perform such an evaluation. The Brooks Lab is extending FLAIR to incorporate sequence variation, RNA editing, and RNA modification in isoform detection as well as detection of complex gene fusions from long-read sequencing data.

  Meeting number: 2311 656 4503 Password: ySkM7uW6B$5 Join by video system Dial 23116564503@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: 2311 656 4503  
Organized by
NIH Library
Description

This webinar introduces SimBiology as a modeling environment for mechanistic pharmacokinetic (PK), pharmacodynamic (PD), and quantitative systems pharmacology (QSP) modeling and simulation. Participants will learn how to use the SimBiology Model Builder app to build a mechanistic model and how to use the SimBiology Model Analyzer app to calibrate the model to experimental data, as well as perform model predictions. 

This is an introductory-level class taught by MathWorks. No installation of Read More

This webinar introduces SimBiology as a modeling environment for mechanistic pharmacokinetic (PK), pharmacodynamic (PD), and quantitative systems pharmacology (QSP) modeling and simulation. Participants will learn how to use the SimBiology Model Builder app to build a mechanistic model and how to use the SimBiology Model Analyzer app to calibrate the model to experimental data, as well as perform model predictions. 

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

Organized by
NIH Library
Description

Large language models (LLMs) are artificial intelligence (AI) algorithms that employ deep learning and extensive data sets to create new content. LLMs offer many possible applications in the biomedical field, such as the development of chatbots for use by clinicians, patients, and researchers. Join this roundtable discussion to learn about current use cases of LLMs at NIH. The program will begin with brief presentations by our panelists, followed by an open discussion:

<Read More

Large language models (LLMs) are artificial intelligence (AI) algorithms that employ deep learning and extensive data sets to create new content. LLMs offer many possible applications in the biomedical field, such as the development of chatbots for use by clinicians, patients, and researchers. Join this roundtable discussion to learn about current use cases of LLMs at NIH. The program will begin with brief presentations by our panelists, followed by an open discussion:

Alicia Lillich, NIH Library 
Introduction to Large Language Models (LLMs)

Trey Saddler, NIEHS
ToxPipe: Semi-Autonomous AI Integration of Diverse Toxicological Data Streams

Mike A. Nalls, Ph.D., NIA
LLMs to Accelerate Tedious Tasks in Research

Nathan Hotaling, Ph.D., NCATS
Applications of Retrieval Augmented Generative AI to Scientific Discovery, Scientific Management, and Code Development and Maintenance at NCATS

Nicole Sroka, NLM
NLM GenAI Pilot: Customer Response Case Study

Steevenson Nelson, Ph.D., OD
Trans IRP Contract Tool (Updates)

Nick Asendorf, Ph.D., NHLBI
NHLBI Chat

Organized by
NIH Library
Description

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read and edit, less prone to errors, and allows it to run more efficiently. This 90-minute advanced class will provide an in-depth look at using and writing macros in SAS. Topics covered in this class include macro function, using SQL and Data Step to create macro variables, indirect references to macro variables, defining Read More

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read and edit, less prone to errors, and allows it to run more efficiently. This 90-minute advanced class will provide an in-depth look at using and writing macros in SAS. Topics covered in this class include macro function, using SQL and Data Step to create macro variables, indirect references to macro variables, defining and calling a macro, macro variable scope, conditional processing, and iterative processing. 

Description

Join us for an engaging training session where we will examine the similarities and differences between machine learning and statistical differential gene expression (DGE) analysis using Qlucore Omics Explorer. Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA, proteomics, metabolomics, as well as enabling the use of machine learning to obtain a deeper understanding of data.
 
At the end Read More

Join us for an engaging training session where we will examine the similarities and differences between machine learning and statistical differential gene expression (DGE) analysis using Qlucore Omics Explorer. Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA, proteomics, metabolomics, as well as enabling the use of machine learning to obtain a deeper understanding of data.
 
At the end of this class, participants will
 
·      Have a deeper understanding of RNA-seq data analysis and understand how to leverage both machine learning and statistical methods to obtain more comprehensive insights.
·      Know the different gene selection methods used by machine learning and statistical DGE analysis.
·      Know how integrating machine learning with DGE analysis can provide additional insights and enhance your research findings.
·      Be able to describe steps for applying machine learning to enhance insight extraction from RNA-seq data.
 
Experience using Qlucore Omics Explorer is not needed to attend. Submit a ticket with service.cancer.gov to get this software installed on personal computer.

Meeting information:

https://cbiit.webex.com/cbiit/j.php?MTID=m11ad78fb6a8303d5d72cffe7c9abfb3a 
Meeting number:
2307 302 4819

Join by video system
Dial 23073024819@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: 2307 302 4819

Organized by
NIH Library
Description

Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. This class will demonstrate integrated development and learning (IDE) platforms for learning Python, the fundamentals of Python coding, and why it is advantageous to develop these skills.  The session will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab.  It will also provide an overview of Read More

Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. This class will demonstrate integrated development and learning (IDE) platforms for learning Python, the fundamentals of Python coding, and why it is advantageous to develop these skills.  The session will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab.  It will also provide an overview of programming constructs needed to learn Python. Finally, this class will demonstrate why these skills can boost productivity, rigor, and transparency in reporting research findings. 

Organized by
NIH Library
Description

What are common statistical analyses for continuous data? Can you check whether your continuous outcome is normally distributed? What are the methods when the data are not normal? How do you model the outcome with multiple predictors in regression?

This is a two-hour lecture intended for those doing basic data analysis using R. Basic R programming is a pre-requisite for this course, as is knowledge of basic statistical concepts, such as mean Read More

What are common statistical analyses for continuous data? Can you check whether your continuous outcome is normally distributed? What are the methods when the data are not normal? How do you model the outcome with multiple predictors in regression?

This is a two-hour lecture intended for those doing basic data analysis using R. Basic R programming is a pre-requisite for this course, as is knowledge of basic statistical concepts, such as mean and p-value from statistical hypothesis testing.  This class will be taught by the Clinical Center's Biostatistics and Clinical Epidemiology Service (CC/BCES).

The learning outcomes include: 

  • calculating and displaying descriptive statistics, such as center and spread of distribution and boxplots 
  • recognizing common continuous probability density functions
  • estimating mean and confidence intervals for the center of normally and non-normally distributed data 
  • hypothesis testing for one-sample and two-sample 
  • linear regression 
  • the F-distribution and one-way ANOVA

R code snippets will be shared during the lecture and within lecture notes. The class will be recorded, so you can go back to the material as you begin to do your own modeling. During the class, time will be devoted to explaining the concepts, and code snippets and output and references will be provided for in-depth material. 

Preclass Requirements: You must take the basic R programming and statistical inference – Part I classes as pre-requisite through the NIH Library or have acquired the equivalent knowledge elsewhere prior to registering for this class.

Statistical Software: We will be using R and RStudio for our statistical analysis. R is open source and free. There are versions for Mac OSX, Windows, and Linux. You can download it from https://cran.r-project.org/. Additionally, we will be using RStudio as a graphical interface for R. RStudio is free for everyone to download at https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming.

Distinguished Speakers Seminar Series

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Organized by
BTEP
Description
Dr. Irizarry will share findings demonstrating limitations of current
workflows that are popular in single cell RNA-Seq data analysis.
Specifically, he will describe challenges and solutions to dimension
reduction, cell-type classification, and statistical significance
analysis of clustering. Dr. Irizarry will end the talk describing some of his
work related to spatial transcriptomics. Specifically, he will describe
approaches to cell type annotation that account for presence of
multiple cell-types Read More
Dr. Irizarry will share findings demonstrating limitations of current
workflows that are popular in single cell RNA-Seq data analysis.
Specifically, he will describe challenges and solutions to dimension
reduction, cell-type classification, and statistical significance
analysis of clustering. Dr. Irizarry will end the talk describing some of his
work related to spatial transcriptomics. Specifically, he will describe
approaches to cell type annotation that account for presence of
multiple cell-types represented in the measurements, a common
occurrence with technologies such as Visium and SlideSeq. He will
demonstrate how this approach facilitates the discovery of spatially
varying genes. Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m9dcd9ce21f4fa6b1a8e2d998a88c2c2b    Meeting number: 2317 712 9095 Password: gUKZzp3u76? Join by video system Dial 23177129095@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: 2317 712 9095  
Organized by
NIH Library
Description

This in-person hands-on workshop will introduce the Ingenuity Pathway Analysis (IPA), which is available to access from the NIH Library. IPA can be used identify biological relationships, mechanisms, pathways, functions, and diseases most relevant to experimental datasets.

Upon completion of this workshop, participants will  be to able compare different groups at different time points and treatments, perform Analysis Read More

This in-person hands-on workshop will introduce the Ingenuity Pathway Analysis (IPA), which is available to access from the NIH Library. IPA can be used identify biological relationships, mechanisms, pathways, functions, and diseases most relevant to experimental datasets.

Upon completion of this workshop, participants will  be to able compare different groups at different time points and treatments, perform Analysis Match to compare user data with public data sources, and generate IPA Networks using genes and diseases of interest. 

Session 1 (IPA): 10:00 AM to 12:00 PM

In this session, participants will learn about bioinformatics resources from the NIH Library and learn how to perform pathway analysis using IPA.

Lunch: 12:00 PM to 12:45 PM

Lunch on your own

Session 2 (IPA): 1:00 PM to 2:30 PM

In this session, participants will extend the learning from Session 1 and learn how to mine IPA database for novel discoveries.

Session 3 (CLC): 2:30 PM to 4:00 PM

In this session, participants will learn about CLC Genomics Workbench, including a live demo of the basic features and main functionalities.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

Registrants will receive an email with information and instructions to install and verify access to IPA before the class.  If you register the day before the class, you may not have time to download and properly install the necessary software. If you do not have the software installed, this training will be demo only.

Organized by
NIH Library
Description

In this in-person session, participants will have an opportunity to discuss their own research and use of Qiagen products with Qiagen scientists.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

In this in-person session, participants will have an opportunity to discuss their own research and use of Qiagen products with Qiagen scientists.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

AI in Biomedical Research @ NIH Seminar Series

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Organized by
BTEP
Description

CARD is a collaborative initiative of the National Institute on Aging and the National Institute of Neurological Disorders and Stroke that supports basic, translational, and clinical research on Alzheimer’s disease and related dementias. CARD’s central mission is to initiate, stimulate, accelerate, and support research that will lead to the development of improved treatments and preventions for these diseases.

Alternative Meeting Information:  Meeting number: 2310 497 7985 Password: mjPjjmi$473 Join by video Read More

CARD is a collaborative initiative of the National Institute on Aging and the National Institute of Neurological Disorders and Stroke that supports basic, translational, and clinical research on Alzheimer’s disease and related dementias. CARD’s central mission is to initiate, stimulate, accelerate, and support research that will lead to the development of improved treatments and preventions for these diseases.

Alternative Meeting Information:  Meeting number: 2310 497 7985 Password: mjPjjmi$473 Join by video system Dial 23104977985@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: 2310 497 7985  
Organized by
NIH Library
Description

During this webinar, participants will enhance their technical skills and proficiency with MATLAB by navigating online MATLAB resources designed to augment the learning experience and problem-solving capabilities, including documentation, examples, and community forums. In addition, this webinar will also present a preview of upcoming webinars, featuring cutting-edge topics and expert insights. 

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

During this webinar, participants will enhance their technical skills and proficiency with MATLAB by navigating online MATLAB resources designed to augment the learning experience and problem-solving capabilities, including documentation, examples, and community forums. In addition, this webinar will also present a preview of upcoming webinars, featuring cutting-edge topics and expert insights. 

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

July

AI in Biomedical Research @ NIH Seminar Series

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Organized by
BTEP
Description

Kerry Goetz, Ph.D.

Meeting number: 2302 034 0947 Password: juFCdpx$627 Join by video system Dial 23020340947@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: 2302 034 0947  

Kerry Goetz, Ph.D.

Meeting number: 2302 034 0947 Password: juFCdpx$627 Join by video system Dial 23020340947@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: 2302 034 0947