Abstract:
Deep learning is revolutionizing the prediction of protein tertiary structure and is close to solve
this grand challenge hanging over the scientific world for many years. In this talk, I will describe
how this technology emerged in the field, how it overcame various technical hurdles to reach a
high accuracy of predicting protein contacts/distances and tertiary structures, and where it is
going now. I will use the development of our MULTICOM protein structure prediction system
ranked among top methods in the last two rounds of CASP protein folding competition as well
as several other state-of-the-art methods as examples to illustrate the process. Moreover, I will
present our latest development of deep learning methods to tackle another grand scientific
challenge – prediction of protein-protein interactions and quaternary structures of protein
complexes, for which revolutionary deep learning technologies will likely emerge in the next few
years as what had happened in the field of protein tertiary structure prediction.

Bio:

Dr. Jianlin Cheng is the Thompson Professor in the Department of Electrical Engineering and
Computer Science at the University of Missouri – Columbia, USA. He earned his PhD in computer science from the University of California, Irvine in 2006. His research is focused on
bioinformatics and machine learning. Dr. Cheng has authored or co-authored 157 journal articles (https://scholar.google.com/citations?user=t9MY6lwAAAAJ&hl=en&oi=ao), which have been cited 13,000 times and have an h-index of 51. His protein structure prediction method – MULTICOM – was consistently ranked among the top methods in the last seven rounds of Critical Assessments of Structure Prediction (CASP8-14) from 2008 to 2020. His research has been supported by the National Institutes of Health (NIH), National Science Foundation (NSF) and Department of Energy (DoE). Dr. Cheng was a recipient of a 2012 NSF CAREER award.

Join Zoom Meeting
https://umd.zoom.us/j/97941931766

Meeting ID: 979 4193 1766
One tap mobile
+13017158592,,97941931766# US (Washington DC)
+13126266799,,97941931766# US (Chicago)

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register Now

Faculty: Eliezer M. Van Allen, MD – Harvard University/Dana-Farber Cancer Institute/BROAD; NCI Cancer Moonshot IOTN

Moderator: Kellie N. Smith, PhD – Johns Hopkins School of Medicine

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

Register

Description:
In this advanced FlowJo cytometry webinar participants will learn how to move beyond the basics of the application to conduct in-depth analyses. FlowJo can accommodate high dimension, high throughput flow, or mass cytometry data. This course will address the use of plug-ins to extend the functionality of FlowJo, including Sunburst, MiST, TriMap, UMAP, Phenograph, FlowSOM, etc. Other topics will look at down sampling, concatenating, and exporting to isolate subset populations, as well as tools and tips on clustering to show meaningful results.

Speaker: Dr. Christian Aguilera-Sandoval, FlowJo, LLC

For questions, contact Dr. Daoud Meerzaman.

Register

Session Description
DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help with specific projects. To schedule an individual meeting, please email the instructor.

Registration

WebEx

Abstract: Heterogeneity poses a major challenge in translational research. For example, inter-tumor heterogeneity limits the biomarker discovery and intra-tumor heterogeneity enables therapeutic resistance. Moreover, in some cancers driver mutations are insufficient to account for the widespread transcriptional variation responsible for these outcomes. Thus, new computational tools to model transcriptional variation are essential. To address this we develop an innovative computational framework, Expression Variation Analysis (EVA), to model transcriptional dysregulation in cancer. Briefly, EVA quantifies transcriptional heterogeneity for one set of samples or cells from one phenotype using the expected dissimilarity between pairs of expression profiles. U-statistics theory can then quantify the statistical significance of the difference in transcriptional heterogeneity between phenotypes. We apply EVA to perform a comprehensive characterization of transcriptional variation in head and neck squamous cell carcinoma (HNSCC). At a pathway level, transcriptional variation in HNSCC tumors is higher than normal controls. Applying EVA to integrate ChIP-seq data with RNA-seq reveals that these pervasive transcriptional differences occur in enhancers. Adapting EVA to single cell data demonstrates greater transcriptional heterogeneity in HNSCC primary tumors than lymph node metastasis consistent with a clonal outgrowth. Similar adaptation of the framework to intra-tumor heterogeneity from spatial transcriptomics data demonstrates transition in hormone receptor pathways between primary breast tumors and premalignant lesions. Thus, we demonstrate that the statistical framework from EVA enables differential heterogeneity analysis in cancer ranging from pathway dysregulation, epigenetic regulation, single cell analysis, and spatial molecular data. This algorithm provides a critical framework to model the hidden multi-molecular mechanisms underlying the complex patient outcomes that are pervasive in cancer.

Register

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

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

Register

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

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

The National Institute of Allergy and Infectious Diseases (NIAID) is excited to advance discovery and innovation in infectious diseases and immune-mediated disorders research by leveraging data and data science approaches. Towards this end, NIAID will conduct a series of ideas and innovation webinars that bring together experts and stakeholders in data science, infectious diseases, immunology, and immune-mediated disorders.

Through the webinar series, participants will have the opportunity to provide insights into the current landscape of data science research and development, as well as offer ideas that promise to shape the future of data-driven immune-mediated and infectious disease research. The webinar series will serve as a platform for collaboration, idea generation, and networking among participants and generate foundational materials that is expected to inform the prospective role of data science in advancing NIAID’s mission.

Our expert panel will engage in a moderated discussion following short talks where they will define the traditional silos that may impede broad data sharing and highlight examples of where breaking those silos facilitated advancement that otherwise could not have been achieved.

Invited Speakers include Dr. Raphael Gottardo (Fred Hutchinson Cancer Research Center), Dr. Alexa McCray (Harvard Medical School), Dr. Ewan Harrison (University of Cambridge). Moderated by Dr. Stephany Duda (Vanderbilt University) and Dr. Purvesh Khatri (Stanford University).

please register to receive meeting link

REGISTRATION :  https://zoom.us/webinar/register/WN_jjHo246UQieRTKWtDdSHug

AGENDA : https://apply.hub.ki/datascience4niaid/

CONTACTS: Event Organizing Committee (NIAIDODSET@niaid.nih.gov)

Presenter:

Sai Lakshmi Subramanian

Program Manager, Cancer Genomics Cloud

Seven Bridges

Abstract

The Cancer Genomics Cloud powered by Seven Bridges (CGC) is a NCI-funded cloud resource that provides a unified platform for cancer data analysis by co-localizing three components within the cloud: 1) large cancer datasets including TCGA, CPTAC and several others from CRDC data nodes; 2) >500 bioinformatics tools and best-practice workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. The user-friendly portal of CGC allows the researchers to browse, query and filter datasets of interest and also bring their own data for collaborative analysis in the context of other publicly available data. In addition to the simplicity of data access and management, the CGC provides the flexibility to bring private tools, and the ability to complete reproducible and interactive analyses, all with the speed of cloud computing resources without needing any cloud provider accounts or managed billing. Using the power of Connected Cloud Storage, datasets residing in Amazon or Google Cloud can be easily attached as volumes. Interactive analysis of data can be performed using RStudio, along with Jupyter notebooks and is tailored to maximize user experience (including billing controls, flexibility, etc). With a keen focus on interoperability, the CGC has implemented services to support the technical standards recommended by the Global Alliance for Genomics and Health (GA4GH). Altogether, these added features enable a network of findable, accessible, interoperable and reusable (FAIR) datasets, workflows, and services towards making cancer data analysis faster, and more easily available for all. In this webinar, we will demonstrate the features available in the CGC for optimizing analysis costs on the cloud and also showcase new workflows for proteomics analysis.

JOIN WEBEX MEETING
https://cbiit.webex.com/cbiit/j.php?MTID=mc85fd4f00b48b1767e287901319a42cd
Meeting number (access code): 180 425 7227

Meeting password: 6pSMQPBS$43

TAP TO JOIN FROM A MOBILE DEVICE (ATTENDEES ONLY)
+1-650-479-3207,,1804257227## tel:%2B1-650-479-3207,,*01*1804257227%23%23*01* Call-in toll number (US/Canada)

Register

Session Description
Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. This introductory class will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. The class will also discuss the differences between animal models, research organisms, and model organisms, and will review requirements and resources for the NIH Model Organism Sharing Policy.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data.

Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure abundance of transcripts; perform differential expression analysis; and visualize the output of RNA-seq analyses.

Register

Description:

IPA’s Analysis Match Explorer was launched in March 2020 and this upgrade is being evaluated for purchase by NCI. NCI scientists will have no-charge access to Analysis Match Explorer for a limited time. This seminar will highlight the additional capabilities provided by Land Explorer for IPA and how it can be used to enhance your projects. Your feedback to BTEP and CBIIT on the benefit you feel these additional features might provide for you is greatly appreciated

Land Explorer for IPA provides access to curated ‘omic data from hundreds of thousands of samples and functions as a complement to the curated literature in the QIAGEN Knowledge Base. Through simple queries and by utilizing sample level metadata, you can:

§ Seamlessly jump from IPA into more granular sample- and gene-level details in Land Explorer, the web-based portal to OmicSoft’s massive Lands databases: OncoLand and DiseaseLand.
§ Explore ‘omics datasets from GTEx, the CCLE, TCGA, and thousands of individual cancer studies from GEO and other repositories.
§ Navigate from a gene of interest in IPA to quickly discover its tissue or cell expression, the diseases and treatments that cause it to be up-or-down-regulated, the cancers in which it is frequently mutated, the effect of mutations on patient survival and much more.
§ Easily determine the expression of a gene, where a gene is observed to be differentially expressed, and additional ‘omic characteristics such as somatic DNA alterations.
§ Query the Lands Datasets even without your own study data

Speaker: Dr. Eric Seiser (Sr. Field Application Scientist – QIAGEN)

POC: Daoud Meerzaman

Register/Join

Kinase inhibitors have been intensively studied and used effectively for cancer treatment for decades. Yet, despite our progress in understanding kinases in oncology, more needs to be known to better predict how and which inhibitor will work best in any given patient at any given time. Phosphoproteomic data could hold the key for such precision medicine, allowing clinicians to select the best medication for each patient.

Dr. Kristin Naegle of the University of Virginia will describe a statistical and graph-theoretic approach to predicting kinase activity. She will show how her team developed an algorithm using phosphoproteomic data from breast cancer tumor biopsies and PDX models. They found that HER2-negative patients were more likely to respond to therapy due to HER2 activity; whereas HER2-positive patients, which lacked net HER2-activity, did not respond to therapy. Dr. Naegle will describe these findings and other work in the field of phosphoproteomics.

Dr. Kristen Naegle is an associate professor of biomedical engineering, computer science and engineering, and resident member of the Center for Public Health Genomics at the University of Virginia. She received her doctorate from the Massachusetts Institute of Technology in biological engineering and was subsequently trained as a postdoctoral associate at the Koch Institute for Integrative Cancer Research.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Registration:  https://btep.ccr.cancer.gov/classes/ai_one/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m61e3ea39fde3a9a94a96efeaa519a656

Description: This talk will introduce basic concepts in building small molecule property prediction using machine learning models trained on data collected from experimental assays.  Practical challenges will be considered, starting with limitations in data collection and curation through to model selection for property prediction applications.  The ATOM (Accelerating Therapeutics for Opportunities in Medicine) Modeling Pipeline (AMPL) will be used to provide concrete examples for building models and data visualization.

Presenter: Jonathan Allen PhD, Computational Scientist, Lawrence Livermore National Laboratory

Webex

Are you clear on how deep learning fits into machine learning? Confused about the difference between training, testing, and validation datasets? Join us for an overview of common machine learning terminology. While this is not a formal introduction to machine learning, we will introduce concepts in a logical order so beginners can become familiar with machine learning jargon and get started!

Presenter:   Andrew Weisman, PhD, High Performance Computing Analyst, Frederick National Laboratory for Cancer Research (FNLCR) 

Questions?  Contact the NCI Data Science Learning Exchange 

(NCIDataScienceLearningExchange@mail.nih.gov

Register

Description:

This webinar will highlight UCSC Xena’s newest features including genome-wide differential gene expression analysis, violin plots, and a simpler way to filter and subgroup.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Xena showcases seminal cancer genomics datasets from TCGA and the Pan-Cancer Atlas, as well as the GDC, PCAWG, and ICGC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modality, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, clinical and phenotypic annotations. Browser features include the high performance Visual Spreadsheet, dynamic Kaplan-Meier survival analysis, genome-wide differential gene expression analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and live bookmarks. Researchers can use the same visualizations to view their own data privately and securely in Xena.

Xena can help you answer questions like:
* Is over-expression of geneA associated with lower survival in these two cancer types?
* Is geneB differentially expressed in TCGA tumor vs GTEx normal?
* What are the most differentially expressed genes for the subgroups I just made?
* What is the relationship between expression, mutation, copy number, etc for these genes?

This webinar will include a live demonstration of Xena. Feel free to follow along in either Chrome or Firefox.

For questions please contact Daoud Meerzaman

Presenter:  Mary Goldman, Design and Outreach Engineer for UCSC Xena, of the UC Santa Cruz Genomics Institute

Register Now

Faculty:  Shannon McWeeney, PhD – Oregon Health & Science University; NCI Cancer Moonshot DRSN

Moderator:  Santosh Putta, PhD

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

Register

Description: Over the last 15 years the Gene Set Enrichment Analysis (GSEA) software and its companion gene set collections, the Molecular Signatures Database (MSigDB) have served as key resources for interpreting the biological significance of expression changes in large transcriptomic datasets. The GSEA-MSigDB project offers free and open-source tools like GSEA – and its single-sample equivalent ssGSEA – and a large database of gene signatures curated to aid in both disease research and understanding basic biology. Our team continues to expand the utility of these resources through the additions of new analysis tools, gene sets, and integrations with other resources. This webinar will provide an introduction to some of the basics of the GSEA method, including various options for how to run GSEA, introduce some of the numerous resources available for analysis in the Molecular Signatures Database, and explore how to analyze and interpret enrichment results.

Presenter: Dr. Anthony Castanza Curator, Molecular Signatures Database Mesirov Lab, Department of Medicine University of California, San Diego

POC: Daoud Meerzaman

Registration: https://btep.ccr.cancer.gov/classes/ai_two/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m8f1460fe5079163dd2a2a4e2641c227f

Description: Deep learning is a subclass of machine learning which has shown excellent performance in learning tasks on unstructured data, such as digital images. This talk consists of two parts. In the part , we will discuss some recent development of machine learning, with emphasis on the cellular object segmentation and tracking, image classification, and image restoration. The second part focuses on applications in digital pathology where we will talk about data acquisition, curation, and cleaning, as well as approaches to deep-learning. By the end of this part, you should be aware of potential pitfalls than confound results and have a better understanding of what it takes to carry out a deep-learning project in digital pathology.

Presenters: Gianluca Pegoraro PhD (Staff scientist) , G Tom Brown MD/PhD (Staff clinician), Center for Cancer Research, NCI

Register Now

Faculty: Dana Pe’er, PhD – Memorial Sloan Kettering Cancer Center; NCI Cancer Moonshot HTAN
Moderator: Daniel Wells, PhD – Immunai

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

Registration: https://btep.ccr.cancer.gov/classes/ai_three/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m933690e1ccdbfd15ae3f75d1cbec3b95

Description: This talk will describe machine learning and deep learning methods to analyze bulk and single-cell RNA sequencing data, as well as deep learning models that integrate epigenetic data to decipher the regulatory networks underlying gene expression.

Presenter: Avantika Lal PhD, Senior Scientist | Deep Learning and Genomics | NVIDIA

Register Now

Faculty: Michael Angelo, MD, PhD – Stanford University, Moonshot Cancer Atlas

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member