ncibtep@nih.gov

Bioinformatics Training and Education Program

Classes & Events

class_id details description start_date Venues learning_levels Topic Tags delivery_method presenters Organizer seminar_series class_title
1530
Organized By:
CBIIT
Description

Clustered heat maps are widely used for visualizing patterns in molecular profiling data. But traditional, static heat maps have significant limitations when applied to large datasets (1000s of elements per axis).

Presented in this talk will be the Next-Generation Clustered Heat Map (NG-CHM) system of highly interactive clustered heat maps for addressing these limitations. Also provided will be all the capabilities expected of a state-of-the-art heat map system.

NG-CHMs enable ...Read More

Clustered heat maps are widely used for visualizing patterns in molecular profiling data. But traditional, static heat maps have significant limitations when applied to large datasets (1000s of elements per axis).

Presented in this talk will be the Next-Generation Clustered Heat Map (NG-CHM) system of highly interactive clustered heat maps for addressing these limitations. Also provided will be all the capabilities expected of a state-of-the-art heat map system.

NG-CHMs enable the user to navigate large omic databases, zooming to drill down on detailed patterns, link out to dozens of external metadata resources, produce high-resolution graphics, and preserve all metadata needed to reproduce the map at a later time. They have proved valuable in many large-scale NIH projects. Data types covered by NG-CHMs have included essentially all the phenotypic genotypic characteristics currently measured at the DNA, RNA, protein, and metabolite levels, in both bulk and single-cell studies.
 
NG-CHMs have been used by thousands of individual researchers and have been incorporated into a variety of public websites. The presentation will conclude with a brief summary of future plans.

For questions contact Daoud Meerzaman or Kayla Strauss.

Clustered heat maps are widely used for visualizing patterns in molecular profiling data. But traditional, static heat maps have significant limitations when applied to large datasets (1000s of elements per axis). Presented in this talk will be the Next-Generation Clustered Heat Map (NG-CHM) system of highly interactive clustered heat maps for addressing these limitations. Also provided will be all the capabilities expected of a state-of-the-art heat map system. NG-CHMs enable the user to navigate large omic databases, zooming to drill down on detailed patterns, link out to dozens of external metadata resources, produce high-resolution graphics, and preserve all metadata needed to reproduce the map at a later time. They have proved valuable in many large-scale NIH projects. Data types covered by NG-CHMs have included essentially all the phenotypic genotypic characteristics currently measured at the DNA, RNA, protein, and metabolite levels, in both bulk and single-cell studies. NG-CHMs have been used by thousands of individual researchers and have been incorporated into a variety of public websites. The presentation will conclude with a brief summary of future plans. For questions contact Daoud Meerzaman or Kayla Strauss. 2024-06-20 10:00:00 Online Any Heat Maps Online Bradley Broom (MD Anderson Cancer Center) CBIIT 0 Next-Generation Clustered Heat Maps
1495
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.

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. 2024-06-20 11:00:00 Online Any R programming,Statistics Online Nusrat Rabbee (NIH/CC) NIH Library 0 Statistical Methods for Continuous Data Analysis Using R
1426
Distinguished Speakers Seminar Series

Join Meeting
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  
Dr. Irizarry will share findings demonstrating limitations of currentworkflows that are popular in single cell RNA-Seq data analysis.Specifically, he will describe challenges and solutions to dimensionreduction, cell-type classification, and statistical significanceanalysis of clustering. Dr. Irizarry will end the talk describing some of hiswork related to spatial transcriptomics. Specifically, he will describeapproaches to cell type annotation that account for presence ofmultiple cell-types represented in the measurements, a commonoccurrence with technologies such as Visium and SlideSeq. He willdemonstrate how this approach facilitates the discovery of spatiallyvarying 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   2024-06-20 13:00:00 Online Webinar Any Biomarkers,Diagnostics Online Rafael Irizarry Ph.D. (Harvard) BTEP 1 Statistical Methods for Single-Cell RNA-Seq Analysis and Spatial Transcriptomics
1520
Organized By:
CBIIT
Description

Register for the June Emerging Technologies Seminar to hear from Dr. Dana Pe’er of the Memorial Sloan Kettering Cancer Center. She will describe new bioinformatics tools for exploring the complex tumor microenvironment. The Human Tumor Atlas Network, an NCI-funded Cancer MoonshotSM initiative, supports these tools with their 3D tumor atlases.

The Read More

Register for the June Emerging Technologies Seminar to hear from Dr. Dana Pe’er of the Memorial Sloan Kettering Cancer Center. She will describe new bioinformatics tools for exploring the complex tumor microenvironment. The Human Tumor Atlas Network, an NCI-funded Cancer MoonshotSM initiative, supports these tools with their 3D tumor atlases.

The NCI Emerging Technologies Seminar Series highlights novel technologies supported through NCI awards that could transform cancer research and clinical care.

To stay apprised of updates to this event and the latest from NCI about data science, sign up to receive our weekly email.

Register for the June Emerging Technologies Seminar to hear from Dr. Dana Pe’er of the Memorial Sloan Kettering Cancer Center. She will describe new bioinformatics tools for exploring the complex tumor microenvironment. The Human Tumor Atlas Network, an NCI-funded Cancer MoonshotSM initiative, supports these tools with their 3D tumor atlases. The NCI Emerging Technologies Seminar Series highlights novel technologies supported through NCI awards that could transform cancer research and clinical care. To stay apprised of updates to this event and the latest from NCI about data science, sign up to receive our weekly email. 2024-06-25 14:00:00 Online Any Image Analysis Online Dana Pe’er Ph.D. (Memorial Sloan Kettering Cancer Center) CBIIT 0 Representing and Embedding Tissue Structures
1496
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.

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. 2024-06-26 10:00:00 NIH Library Training Room, Building 10, Clinical Center, South Entrance Any Pathway Analysis In-Person NIH Library Staff NIH Library 0 NIH Library Workshop: Ingenuity Pathway Analysis (IPA)
1497
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. 

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.  2024-06-27 10:00:00 NIH Library Training Room Building 10 Clinical Center South Entrance Any Pathway Analysis In-Person Qiagen NIH Library 0 NIH Library Workshop: Qiagen Ask Me Anything (AMA)
1532
Organized By:
CBIIT
Description

During this presentation, you will learn about the GenePattern ecosystem, an environment for accessible, reproducible research that has been serving the needs of the cancer genomics community since 2004.

GenePattern hosts hundreds of genomics analysis and visualization tools, presented in a web-based format that requires no programming, along with extensive features for reproducibility and accessibility. 
Available analyses include bulk and single-cell gene expression, gene set enrichment analysis, mutation significance, flow cytometry, ...Read More

During this presentation, you will learn about the GenePattern ecosystem, an environment for accessible, reproducible research that has been serving the needs of the cancer genomics community since 2004.

GenePattern hosts hundreds of genomics analysis and visualization tools, presented in a web-based format that requires no programming, along with extensive features for reproducibility and accessibility. 
Available analyses include bulk and single-cell gene expression, gene set enrichment analysis, mutation significance, flow cytometry, proteomics, general machine learning approaches, and many others.

A notebook interface allows GenePattern analyses to be combined with all the capabilities that Jupyter Notebooks offers. Mr. Reich will describe how scientists can use GenePattern to empower their cancer genomics research.

For questions, contact Daoud Meerzaman or Kayla Strauss.

During this presentation, you will learn about the GenePattern ecosystem, an environment for accessible, reproducible research that has been serving the needs of the cancer genomics community since 2004. GenePattern hosts hundreds of genomics analysis and visualization tools, presented in a web-based format that requires no programming, along with extensive features for reproducibility and accessibility. Available analyses include bulk and single-cell gene expression, gene set enrichment analysis, mutation significance, flow cytometry, proteomics, general machine learning approaches, and many others. A notebook interface allows GenePattern analyses to be combined with all the capabilities that Jupyter Notebooks offers. Mr. Reich will describe how scientists can use GenePattern to empower their cancer genomics research. For questions, contact Daoud Meerzaman or Kayla Strauss. 2024-06-27 10:00:00 Online Any Bioinformatics Software Online Michael Reich (Mesirov Lab UC San Diego) CBIIT 0 The GenePattern Ecosystem for Cancer Genomics and Reproducible Research
1395
AI in Biomedical Research @ NIH Seminar Series

Join Meeting
Organized By:
BTEP
Description

CRISPRbrain is an open-science data commons for functional genomics screens in edited, differentiated human cell types. The platform was created by CARD data scientists in collaboration with the Kampmann Lab at the University of California, San Francisco.

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  

CRISPRbrain is an open-science data commons for functional genomics screens in edited, differentiated human cell types. The platform was created by CARD data scientists in collaboration with the Kampmann Lab at the University of California, San Francisco.

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  
CRISPRbrain is an open-science data commons for functional genomics screens in edited, differentiated human cell types. The platform was created by CARD data scientists in collaboration with the Kampmann Lab at the University of California, San Francisco. 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   2024-06-27 13:00:00 Online Webinar Any AI Online Faraz Faghri Ph.D. (CARD) BTEP 1 CRISPRbrain: A Data Commons for Functional Genomics of Neurodegenerative Diseases
1498
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. 

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.  2024-06-28 11:00:00 Online Any Matlab Online Mathworks NIH Library 0 MATLAB Training and Resources
1513
Organized By:
NIH Library
Description

This two-hour in-person workshop will focus on data wrangling using tidy data principles. Tidy data describes a standard way of storing data that facilitates analysis and visualization within the tidyverse ecosystem. There will be a discussion of what makes data "tidy," and methods for reshaping your data using Read More

This two-hour in-person workshop will focus on data wrangling using tidy data principles. Tidy data describes a standard way of storing data that facilitates analysis and visualization within the tidyverse ecosystem. There will be a discussion of what makes data "tidy," and methods for reshaping your data using dplyr and tidyr functions. 

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

  • Describe the purpose of the dplyr and tidyr packages
  • Select certain columns and rows in a data frame
  • Add new columns to a data frame that are functions of existing columns
  • Use the split-apply-combine concept for data analysis
Requirements

Prior to attending this training, you will need to have:

  1. Installed R and RStudio
  2. Taken the Introduction to R and RStudio training. If not, here are some resources for getting started:
    1. Introduction to R
    2. Introduction to RStudio
    3. Introduction to Scripts in RStudio
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 R and RStudio before the training.  If you register the day before the training, 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.

This two-hour in-person workshop will focus on data wrangling using tidy data principles. Tidy data describes a standard way of storing data that facilitates analysis and visualization within the tidyverse ecosystem. There will be a discussion of what makes data "tidy," and methods for reshaping your data using dplyr and tidyr functions.  By the end of this training, attendees will be able to demonstrate how to: Describe the purpose of the dplyr and tidyr packages Select certain columns and rows in a data frame Add new columns to a data frame that are functions of existing columns Use the split-apply-combine concept for data analysis Requirements Prior to attending this training, you will need to have: Installed R and RStudio Taken the Introduction to R and RStudio training. If not, here are some resources for getting started: Introduction to R Introduction to RStudio Introduction to Scripts in RStudio 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 R and RStudio before the training.  If you register the day before the training, 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. 2024-07-08 13:00:00 NIH Library Training Room Building 10 Clinical Center South Entrance Any Data Wrangling In-Person Doug Joubert (NIH Library) NIH Library 0 Data Wrangling Workshop
1514
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 workshop will cover exome sequencing data analysis, followed by tutorials showing the use of exome analysis workflow. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. This workshop will be taught by NCI staff and is open to NIH and ...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 workshop will cover exome sequencing data analysis, followed by tutorials showing the use of exome analysis workflow. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. This workshop will be taught by NCI staff and is open to NIH and HHS staff. 

Participants will have a chance to: independently run basic exome analysis for variant detection, run quality control check on sequencing data, align the sequencing reads to a reference genome, generate alignment statistics and check mapping quality, identify variants, and visualize the exome sequencing data and variants. 

This class is 3 hours and is a mix of lecture and hand-on exercise. 

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 cover exome sequencing data analysis, followed by tutorials showing the use of exome analysis workflow. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. This workshop will be taught by NCI staff and is open to NIH and HHS staff.  Participants will have a chance to: independently run basic exome analysis for variant detection, run quality control check on sequencing data, align the sequencing reads to a reference genome, generate alignment statistics and check mapping quality, identify variants, and visualize the exome sequencing data and variants.  This class is 3 hours and is a mix of lecture and hand-on exercise.  2024-07-09 13:00:00 Online Any Exome Sequencing Online Daoud Meerzaman (CBIIT) NIH Library 0 Exome Sequencing Data Analysis
1512
Join Meeting
Organized By:
BTEP
Description

Please join us for this special event featuring three speakers on the topic of Single Cell Spatial Transcriptomics.

George Zaki, Ph.D., Director, Applied Scientific Computing, BACS, The Frederick National Lab for Cancer Research, NCI/CBIIT

Analysis of SPAtial Single-Cell Datasets using SPAC:  From hypotheses to insights

SPAC is a modular, from raw tabular data ...Read More

Please join us for this special event featuring three speakers on the topic of Single Cell Spatial Transcriptomics.

George Zaki, Ph.D., Director, Applied Scientific Computing, BACS, The Frederick National Lab for Cancer Research, NCI/CBIIT

Analysis of SPAtial Single-Cell Datasets using SPAC:  From hypotheses to insights

SPAC is a modular, from raw tabular data to scientific insights, web-accessible toolkit for analyzing spatial, single-cell datasets derived from multiplex IF-stained, whole-slide images generated by different technologies, such as InSituPlex (Ultivue), Imaging cyTOF (Standard BioTools), and PhenoCycler (AKA CODEX) or Opal TSA (Akoya Biosciences). Researchers use SPAC to build and configure scalable, flexible, multistep analysis pipelines on the web and share them with collaborators using a single click.

 Noemi Kedei, M.D., Facility Head, Staff Scientist, Spatial Imaging Technology Resource (SpITR), NCI CCR OSTR

Formerly known as the Collaborative Protein Technology Resource (CPTR), the Spatial Imaging Technology Resource (SpITR) is an open core supported by the NCI CCR Office of Science and Technology Resources (OSTR) dedicated to establishing and implementing cutting-edge molecular profiling technologies to facilitate discovery, translational, and clinical research. Spatial technologies include Phenocycler Fusion/CODEX for highly multiplex protein detection at single cell resolution and Nanostring CosMx and GeoMx Digital Spatial Profiling (DSP) for protein and transcript detection at single cell and regional level.

 Lichun Ma Ph.D., Stadtman Investigator, Cancer Data Science Laboratory (CDSL), NCI CCR

Spatial Single-cell Dissection of Cellular Neighborhoods in Liver Cancer

Tumor heterogeneity is the observation that cancer cells can show distinct differences from patient to patient, from primary to secondary tumors, or even between cells within the same tumor. This phenomenon is a major barrier to effective cancer interventions. A better understanding of tumor heterogeneity is critical for improving cancer treatment. Using cutting-edge technology in single-cell and spatial ‘omics assays, my research program focuses on developing novel approaches to understanding tumor heterogeneity through the lens of cellular neighborhoods.

Please join us for this special event featuring three speakers on the topic of Single Cell Spatial Transcriptomics. George Zaki, Ph.D., Director, Applied Scientific Computing, BACS, The Frederick National Lab for Cancer Research, NCI/CBIIT Analysis of SPAtial Single-Cell Datasets using SPAC:  From hypotheses to insights SPAC is a modular, from raw tabular data to scientific insights, web-accessible toolkit for analyzing spatial, single-cell datasets derived from multiplex IF-stained, whole-slide images generated by different technologies, such as InSituPlex (Ultivue), Imaging cyTOF (Standard BioTools), and PhenoCycler (AKA CODEX) or Opal TSA (Akoya Biosciences). Researchers use SPAC to build and configure scalable, flexible, multistep analysis pipelines on the web and share them with collaborators using a single click.  Noemi Kedei, M.D., Facility Head, Staff Scientist, Spatial Imaging Technology Resource (SpITR), NCI CCR OSTR Formerly known as the Collaborative Protein Technology Resource (CPTR), the Spatial Imaging Technology Resource (SpITR) is an open core supported by the NCI CCR Office of Science and Technology Resources (OSTR) dedicated to establishing and implementing cutting-edge molecular profiling technologies to facilitate discovery, translational, and clinical research. Spatial technologies include Phenocycler Fusion/CODEX for highly multiplex protein detection at single cell resolution and Nanostring CosMx and GeoMx Digital Spatial Profiling (DSP) for protein and transcript detection at single cell and regional level.  Lichun Ma Ph.D., Stadtman Investigator, Cancer Data Science Laboratory (CDSL), NCI CCR Spatial Single-cell Dissection of Cellular Neighborhoods in Liver Cancer Tumor heterogeneity is the observation that cancer cells can show distinct differences from patient to patient, from primary to secondary tumors, or even between cells within the same tumor. This phenomenon is a major barrier to effective cancer interventions. A better understanding of tumor heterogeneity is critical for improving cancer treatment. Using cutting-edge technology in single-cell and spatial ‘omics assays, my research program focuses on developing novel approaches to understanding tumor heterogeneity through the lens of cellular neighborhoods. 2024-07-11 13:00:00 Online Webinar Any Single Cell Technologies,Spatial Transcriptomics Online George Zaki Ph.D. (FNLCR CBIIT),Lichun Ma Ph.D. (CCR CDSL),Noemi Kedei M.D. (CCR SpITR) BTEP 0 SPECIAL EVENT: Single Cell Spatial Transcriptomics
1515
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. 

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.  2024-07-12 11:00:00 Online Any Python Programming Online Cindy Sheffield (NIH Library) NIH Library 0 Python for Data Science: How to Get Started, What to Learn, and Why
1533
Organized By:
NIA Biomedical Data Science Series
Description

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training.

Day 1: Topics to be covered included:  What is accessibility and why is it important? Learn the basics of creating accessible documents and guidance on how to add ...Read More

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training.

Day 1: Topics to be covered included:  What is accessibility and why is it important? Learn the basics of creating accessible documents and guidance on how to add captioning, ASL Interpretation, and other accessibility features.

Class requirements: none 
 
Accessibility Statement: This training will be held virtually on Zoom. Closed captioning will be available. Individuals who are needing ASL interpretation and/or other reasonable accommodations should contact Kelli Van Zee, kelli.vanzee@nih.gov. Requests should be made at least five days in advance. 

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training. Day 1: Topics to be covered included:  What is accessibility and why is it important? Learn the basics of creating accessible documents and guidance on how to add captioning, ASL Interpretation, and other accessibility features. Class requirements: none  Accessibility Statement: This training will be held virtually on Zoom. Closed captioning will be available. Individuals who are needing ASL interpretation and/or other reasonable accommodations should contact Kelli Van Zee, kelli.vanzee@nih.gov. Requests should be made at least five days in advance.  2024-07-15 14:30:00 Online Any Data Sharing,ASL Online Kelly Ohaver and Kelli Van Zee (NIA) NIA Biomedical Data Science Series 0 Access Ability: Creating and Sharing Accessible Information to All (Day 1)
1534
Organized By:
NIA Biomedical Data Science Series
Description

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training.

Days 2: Day 2 includes a more in-depth look at requirements for visual elements like color contrast, descriptions, and other accessibility features for charts, graphs, figures. There will also be ...Read More

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training.

Days 2: Day 2 includes a more in-depth look at requirements for visual elements like color contrast, descriptions, and other accessibility features for charts, graphs, figures. There will also be a few tips for R Studio, Python, and GraphPad. Additionally, you will learn how to create accessible visual elements for documents. 

Class requirements: none 
 
Accessibility Statement: This training will be held virtually on Zoom. Closed captioning will be available. Individuals who are needing ASL interpretation and/or other reasonable accommodations should contact Kelli Van Zee, kelli.vanzee@nih.gov. Requests should be made at least five days in advance. 

Learn about creating presentations, graphics, and information that is available to people of all abilities. This training will be held across two sessions on Monday, July 15, from 2:30 – 4:00 and Tuesday, July 16, from 12:30 – 2:00. These classes will count towards 3h of DEIA training. Days 2: Day 2 includes a more in-depth look at requirements for visual elements like color contrast, descriptions, and other accessibility features for charts, graphs, figures. There will also be a few tips for R Studio, Python, and GraphPad. Additionally, you will learn how to create accessible visual elements for documents.  Class requirements: none  Accessibility Statement: This training will be held virtually on Zoom. Closed captioning will be available. Individuals who are needing ASL interpretation and/or other reasonable accommodations should contact Kelli Van Zee, kelli.vanzee@nih.gov. Requests should be made at least five days in advance.  2024-07-16 12:30:00 Online Any ASL,Data Sharing Online Kelly Ohaver and Kelli Van Zee (NIA) NIA Biomedical Data Science Series 0 Access Ability: Creating and Sharing Accessible Information to All (Day 2)
1516
Organized By:
NIH Library
Description

In this webinar, participants will apply deep learning to brain MRI images. They will explore multiple methods of generating models, as well as interrogate them 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 class taught by MathWorks. No installation of MATLAB is necessary. 

In this webinar, participants will apply deep learning to brain MRI images. They will explore multiple methods of generating models, as well as interrogate them 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 class taught by MathWorks. No installation of MATLAB is necessary. 

In this webinar, participants will apply deep learning to brain MRI images. They will explore multiple methods of generating models, as well as interrogate them 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 class taught by MathWorks. No installation of MATLAB is necessary.  2024-07-17 13:00:00 Online Any AI Online Mathworks NIH Library 0 Data Science and AI: Brain MRI Datasets with MATLAB
1517
Organized By:
NIH Library
Description

This class will provide an overview of the data collection process for your review – whether scoping or systematic. The importance of data cleaning for consistency to ensure accurate identification of comparable outcome measures across studies and how to do so will be discussed. We will review the process and tools to use, discuss recommended practices, and share lessons learned. Participants will receive resources and information on recommended practices for performing this important step ...Read More

This class will provide an overview of the data collection process for your review – whether scoping or systematic. The importance of data cleaning for consistency to ensure accurate identification of comparable outcome measures across studies and how to do so will be discussed. We will review the process and tools to use, discuss recommended practices, and share lessons learned. Participants will receive resources and information on recommended practices for performing this important step in your review.

This class will provide an overview of the data collection process for your review – whether scoping or systematic. The importance of data cleaning for consistency to ensure accurate identification of comparable outcome measures across studies and how to do so will be discussed. We will review the process and tools to use, discuss recommended practices, and share lessons learned. Participants will receive resources and information on recommended practices for performing this important step in your review. 2024-07-18 10:00:00 Online Any Data Management Online Jordan Wickstrom NIH Library 0 Collecting and Cleaning Data for Your Review
1528
Organized By:
NIH Library
Description

What are common statistical analyses for binary data? What is the distribution of your binary dependent variable? What is the difference from normally distributed data? How do you model the binary outcome with multiple predictors in a 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 ...Read More

What are common statistical analyses for binary data? What is the distribution of your binary dependent variable? What is the difference from normally distributed data? How do you model the binary outcome with multiple predictors in a 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 rates, proportions, and barplots 
  • recognizing the binomial probability density function as distinct from the normal density function
  • estimating proportion and confidence intervals
  • hypothesis testing for one-sample and two-sample 
  • logistic regression and checking model assumptions 
  • model diagnostics checking and results interpretation

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 from https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming.

What are common statistical analyses for binary data? What is the distribution of your binary dependent variable? What is the difference from normally distributed data? How do you model the binary outcome with multiple predictors in a 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 rates, proportions, and barplots  recognizing the binomial probability density function as distinct from the normal density function estimating proportion and confidence intervals hypothesis testing for one-sample and two-sample  logistic regression and checking model assumptions  model diagnostics checking and results interpretation 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 from https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming. 2024-08-08 11:00:00 Online Any Statistics Online Nusrat Rabbee (NIH/CC) NIH Library 0 Statistical Methods for Binary Data Analysis Using R
1391
Distinguished Speakers Seminar Series

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BTEP
Description

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

Alternative Meeting Information: ...Read More

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

Alternative Meeting Information: Meeting number: 2319 759 4122 Password: Join by video system Dial 23197594122@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: 2319 759 4122  
The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.   Alternative Meeting Information: Meeting number: 2319 759 4122 Password: Join by video system Dial 23197594122@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: 2319 759 4122   2024-08-08 13:00:00 Online Any AI,Precision Medicine Online Olivier Elemento Ph.D. (Weill Cornell Medicine) BTEP 1 Genomes, Avatars and AI: The Future of Personalized Medicine
1394
Distinguished Speakers Seminar Series

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Description

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding ...Read More

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting.

Alternative Meeting Information: Meeting number: 2312 714 2024 Password: GrddnZQ*248 Join by video system Dial 23127142024@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: 2312 714 2024  
Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting. Alternative Meeting Information: Meeting number: 2312 714 2024 Password: GrddnZQ*248 Join by video system Dial 23127142024@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: 2312 714 2024   2024-08-29 13:00:00 Online Webinar Any Cancer genomics,Pediatric Cancer Online Elaine Mardis Ph.D. (Nationwide Children\'s Hospital) BTEP 1 Clinical and Computational Molecular Profiling in Pediatric Cancer Diagnostics
1403
Distinguished Speakers Seminar Series

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BTEP
Description

Dr. O'Neill's research programs employ molecular genetics, genomics and computational approaches to study the mechanisms that maintain, and disrupt, genome stability with a particular focus on repetitive elements. Projects include studying: retroelement transcription and centromere function; novel small RNA biogenesis pathways; and global chromosome and genome changes during instability (such as in cancer and hybrid dysgenesis). In addition, we use a diverse set of rapidly evolving next generation sequencing (NGS) technologies and novel library ...Read More

Dr. O'Neill's research programs employ molecular genetics, genomics and computational approaches to study the mechanisms that maintain, and disrupt, genome stability with a particular focus on repetitive elements. Projects include studying: retroelement transcription and centromere function; novel small RNA biogenesis pathways; and global chromosome and genome changes during instability (such as in cancer and hybrid dysgenesis). In addition, we use a diverse set of rapidly evolving next generation sequencing (NGS) technologies and novel library preparation and computational methodologies for drafting and characterizing genome sequences in efforts to establish broad eukaryotic species as models for studying genome biology. Recently, Dr. O'Neill's lab has expanded their efforts towards applying broad NGS techniques to both model and non-model systems to understand the dynamic response of the genome to environmental queues, such as global warming.

Meeting number: 2315 524 3558 Password: JEexR5Jq@63 Join by video system Dial 23155243558@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: 2315 524 3558
Dr. O'Neill's research programs employ molecular genetics, genomics and computational approaches to study the mechanisms that maintain, and disrupt, genome stability with a particular focus on repetitive elements. Projects include studying: retroelement transcription and centromere function; novel small RNA biogenesis pathways; and global chromosome and genome changes during instability (such as in cancer and hybrid dysgenesis). In addition, we use a diverse set of rapidly evolving next generation sequencing (NGS) technologies and novel library preparation and computational methodologies for drafting and characterizing genome sequences in efforts to establish broad eukaryotic species as models for studying genome biology. Recently, Dr. O'Neill's lab has expanded their efforts towards applying broad NGS techniques to both model and non-model systems to understand the dynamic response of the genome to environmental queues, such as global warming. Meeting number: 2315 524 3558 Password: JEexR5Jq@63 Join by video system Dial 23155243558@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: 2315 524 3558 2024-09-12 13:00:00 Online Webinar Any Cancer genomics,Repetive Elements Online Rachel O\'Neill Ph.D. (Univ. of Connecticut) BTEP 1 Rachel O'Neill
1488
AI in Biomedical Research @ NIH Seminar Series

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BTEP
Description

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods ...Read More

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others. Both clinical and laboratory-based imaging projects will be considered. Please refer to our ongoing projects and prior publications for more information.

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others. Both clinical and laboratory-based imaging projects will be considered. Please refer to our ongoing projects and prior publications for more information. 2024-09-26 13:00:00 Online Webinar Any AI,Image Analysis Online Ismail Baris Turkbey M.D. (NCI CCR AIR) BTEP 1 AI: Baris Turkbey - AIR
1387
Distinguished Speakers Seminar Series

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BTEP
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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 ...Read More

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.

Meeting number: 2312 437 6963 Password: bMrGtiA@933 Join by video system Dial 23124376963@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: 2312 437 6963  
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. Meeting number: 2312 437 6963 Password: bMrGtiA@933 Join by video system Dial 23124376963@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: 2312 437 6963   2024-11-07 13:00:00 Online Webinar Any Online Seth Blackshaw Ph.D. (Johns Hopkins) BTEP 1 Building and Rebuilding the Vertebrate Retina, One Cell at a Time
1422
AI in Biomedical Research @ NIH Seminar Series

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BTEP
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David M. Reif, Ph.D., joined the NIEHS in 2022 as Chief of the Predictive Toxicology Branch (PTB) in the Division of Translational Toxicology (DTT). In this role, he will leverage expertise of the branch in data science, toxicogenomics, spatiotemporal exposures and toxicology, computational methods development, and new approach methods (NAMs) to advance predictive toxicology applications with partners across NIEHS, the interagency Tox21 Program and the Interagency Coordinating Committee on the Validation of Alternative Methods (...Read More

David M. Reif, Ph.D., joined the NIEHS in 2022 as Chief of the Predictive Toxicology Branch (PTB) in the Division of Translational Toxicology (DTT). In this role, he will leverage expertise of the branch in data science, toxicogenomics, spatiotemporal exposures and toxicology, computational methods development, and new approach methods (NAMs) to advance predictive toxicology applications with partners across NIEHS, the interagency Tox21 Program and the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM).

Meeting number: 2318 207 2771 Password: 5DMpVr5Mt5@ Join by video system Dial 23182072771@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: 2318 207 2771  
David M. Reif, Ph.D., joined the NIEHS in 2022 as Chief of the Predictive Toxicology Branch (PTB) in the Division of Translational Toxicology (DTT). In this role, he will leverage expertise of the branch in data science, toxicogenomics, spatiotemporal exposures and toxicology, computational methods development, and new approach methods (NAMs) to advance predictive toxicology applications with partners across NIEHS, the interagency Tox21 Program and the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). Meeting number: 2318 207 2771 Password: 5DMpVr5Mt5@ Join by video system Dial 23182072771@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: 2318 207 2771   2024-11-14 13:00:00 Online Webinar Any AI Online David Reif Ph.D. (NIEHS) BTEP 1 David Reif, Ph.D.
1386
Distinguished Speakers Seminar Series

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BTEP
Description

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 ...Read More

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.

Join information Alternative Meeting Information: Meeting number: 2309 763 3797 Password: GmUAeeZ@236 Join by video system Dial 23097633797@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: 2309 763 3797  
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. Join information Alternative Meeting Information: Meeting number: 2309 763 3797 Password: GmUAeeZ@236 Join by video system Dial 23097633797@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: 2309 763 3797   2024-11-21 13:00:00 Online Any Cancer genomics,Mouse Online Carol Bult Ph.D. (The Jackson Lab) BTEP 1 Pre-clinical Evaluation of Targeted Therapies for Pediatric Cancer