Format: Webinar

We invite you to join in an inspiring conversation between a thought leader from the Cancer Moonshot and creative visualization expert and opportunity to engage with new colleagues from other fields in a discussion on the frontiers of cancer data visualization.  Additional information can be found at the DataViz + Cancer website.

Speakers:

  Benjamin Stokes, Ph.D.

Associate Professor
American University
Civic media scholar, neighborhood game designer
Director of The Playful City Lab
Chief Advisor of interactive media for the Peabody Awards

  Eric Holland, M.D., Ph.D.

Senior VP and Director
Human Biology Division at Fred Hutch
Physician-scientist who develops multidisciplinary approaches to address the molecular basis of brain tumors and new approaches to their treatment

Join Drs. Andrey Fedorov and Hugo Aerts for the upcoming October NCI Imaging and Informatics Community Webinar. Their presentation includes updates to the Imaging Data Commons (IDC)—a repository of the NCI Cancer Research Data Commons—and conversation concerning a cloud-based platform for the dissemination of deep learning models.

Dr. Fedorov will provide the update on IDC and discuss new data sets and new features of the repository. He will also review the work on expanding learning materials, including the application of IDC and cloud computing to support reproducible artificial intelligence (AI) research.

Dr. Aerts will present on the cloud-based platform that NCI and his team are developing for the structured dissemination of deep learning models that is domain-, data-, and framework-agnostic, and can cater to different workflows and contributors’ preferences.

Speakers:

  Andrey Fedorov, Ph.D.

Dr. Fedorov is an associate professor of radiology at Harvard Medical School. His research is in the translation and validation of medical image computing technology in clinical research applications, with a focus on quantitative imaging, imaging informatics, and image-guided interventional procedures. He is currently a co-principal investigator tasked with building the NCI Imaging Data Commons.

  Hugo Aerts, Ph.D.

Dr. Aerts is an associate professor at Harvard University and a full professor at Maastricht University. He is the director of the Artificial Intelligence in Medicine (AIM) Program at Harvard-Mass General Brigham (MGB), a leader in medical AI, and a principal investigator on major NIH-supported efforts, including NCI’s Quantitative Imaging Network and Informatics Technology for Cancer Research initiatives.

Meeting number:2307 428 4731

Speaker:

Steven D Cappell

NIH Stadtman Investigator

Center for Cancer Research – National Cancer Institute – Bethesda MD

My laboratory is integrating quantitative image analysis with genetic, biochemical, and systems biology approaches to understand how cells make fate decisions, such as the decision to enter the cell cycle and proliferate. This is arguably one of the most important decisions mammalian cells have to make with defective regulation leading to cancer or tissue degeneration. The control mechanisms underlying cell cycle regulation are highly dynamic and complex, and we are interested in mapping these dynamic signaling pathways to identify vulnerabilities that can be exploited for therapeutic utility. Our efforts to monitor signaling pathways in real-time and in live cells have already begun to reveal some of the mechanisms involved in cell cycle commitment. Moving forward, we seek to gain a more detailed understanding of the regulatory circuits generating irreversibility in the cell cycle, how these mechanisms are perturbed in cancer cells, and how cancer cells specifically adapt to genetic or pharmacological perturbations to continue to proliferate.

Venue: Building 4 – Room 433 (NIH Bethesda campus) + virtual

Meeting ID: 161 216 1536

Passcode: 169470

Speaker:

Luis G. Carvajal-Carmona, Ph.D.
Professor and Auburn Community Cancer Endowed Chair in Basic Science
Department of Biochemistry and Molecular Medicine
UC Davis Health School of Medicine

Dr. Luis Carvajal-Carmona is Professor and Auburn Community Cancer Endowed Chair in Basic Science in the Department of Biochemistry and Molecular Medicine at UC Davis Health School of Medicine. He also is the leader of the Cancer Center’s Latinos United for Cancer Health Advancement (LUCHA) initiative and the co-director Community Engagement Program at the Clinical and Translational Science Center. His research group at the Carvajal-Carmona Lab is interested in the study of cancer genetic susceptibility. They use genetic, genomic, and functional approaches to identify novel cancer-causing gene and mutations in human populations, to investigate the function of genetic variation associated with disease, and to carry out pre-clinical studies aimed at developing better molecularly guided therapies. In this webinar, Dr. Carvajal-Carmona will be presenting on genetics, genomics, and precision medicine of gastric cancer in Latinos

In this seminar, Project Rōnin’s Vice President of Data Science, Dr. Christine Swisher, will discuss the challenges of ensuring safe and ethical artificial intelligence (AI) in healthcare.

Specifically, Dr. Swisher will examine:

• the need to monitor the real-world impact of AI-based decision-making tools proactively and continuously.
• approaches that result in improved technology reliability and better patient outcomes.

With her Project Rōnin team, Dr. Swisher has delivered AI-based systems that support clinical decision-making and natural language processing innovations. She also holds over 20 patents on machine learning and AI.

The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

Speaker:

Christine Swisher, Ph.D.

Dr. Swisher leads teams of data scientists, statisticians, clinical informaticists, and machine learning experts to build technologies that solve challenging clinical problems. She is passionate about improving patient outcomes and delivering innovations safely and ethically.

For our next CDSL webinar we will have presentations by two CDSL fellows: Ekaterina Kazantseva and Sanna Madan. 

Ekaterina is a master’s student in Dr. Mikhail Kolmogorov’s group and the title of her talk is “Phasing of Partially Resolved Metagenomic Assemblies“.

Sanna is a PhD student working with Dr. Eytan Ruppin and she will give a talk on “Identifying new CAR-T targets from single cell RNA-seq data“.

Abstract (Ekaterina’s talk):

Long-read metagenomic sequencing has recently been used to recover complete bacterial genomes from various complex metagenomic communities. Metagenome assembly algorithms however are still facing challenges in deconvolution of closely-related species and strains. De novo assemblies of highly heterogeneous bacterial species typically result in tangled assembly graphs, where some sequences could be strain-specific, while others represent species-level consensus. Such a partially-collapsed representation of bacterial strains does not take full advantage of the ability of long reads to phase small variants. In this work we present an algorithm called MetaPhase that extends metagenomic phasing approaches to assembly graphs. Our algorithm operates on graph paths rather than single contigs, and iteratively simplifies assembly graphs with newly reconstructed strain contigs. We benchmark our algorithm using mock communities and show that it produces accurate and complete strain-level reconstructions and substantially improves over the initial partially-collapsed assemblies.

Abstract (Sanna’s talk):

Chimeric antigen receptor (CAR) T cell therapy is a powerful and promising tool for unleashing lasting antitumor immunity. While this modality has yielded major clinical success in treating blood cancers, obstacles remain to achieve its potential in solid tumors. In particular, identifying targets that are uniformly expressed across cancer cells and minimally so on normal cells remains a key challenge. Thus, the criteria for ideal CAR-T targets are two-fold: they must (1) be selectively expressed in tumor cells and not on non-tumor cells within the tumor microenvironment (TME), and (2) be lowly expressed across normal human tissues. Mining single cell transcriptomics datasets of solid tumors, we first survey the landscape of current CAR-T targets in the clinic, charting their tumor cell-specificity (termed selectivity score) and expression levels across healthy tissues (termed safety score). Next, we identify cell surface protein-encoding genes whose selectivity and safety scores surpass those of the leading targets in clinics. Subsequently, we put forth that the proteins encoding the genes resulting from our analysis may constitute optimal new targets of CAR-T therapies. Intriguingly, our analysis has yielded an enrichment of targets for head and neck cancer, a cancer type for which there are currently very few unique targets of CAR-T therapies in the clinic. Taken together, this analysis uncovers a large potential of scRNA-seq data in developing precise, selective CAR-T therapies.

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. RStudio also connects with Git and Github, and learners will have a chance to experiment with this integration and understand its advantages for collaboration and version control. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 2 of this class.

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. Participants will learn how to create reproducible documents that combine code, analysis, and narrative. This intermediate-level course is designed to be relevant to students from different disciplines. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 1 of this class.

The call for better data and evidence for decision-making has become very real as evidenced by the Federal Data Strategy, as well as the passage of both the Foundations of  Evidence-based Policymaking Act (Evidence Act) and the CHIPS+ Act.  The challenge to be addressed is finding out not just what data are produced but how they are used – in essence, to build an Amazon.com for data -so that both governments and researchers can quickly find the data and evidence they need. To paraphrase Lee Platt’s aphorism about HP – “If researchers knew what researchers know, they would be three times more productive”

This talk will provide an overview of a massive effort over the past five years which has been focused on finding out how data are being used, to answer what questions, and find out who are the experts, by mining text documents that are hidden in plain sight – in the text of scientific publications, government reports and public documents.

Just as with Amazon, the results are enormously powerful.  The pilot, which is sponsored by agencies such as  NSF’s National Center for Science and Engineering Statistics (NCSES) and the Department of Education’s National Center for Education Statistics (NCES) – has generated a prototype API and a dashboard that can be used – so that, for example, agencies can document dataset use for Congress and the public, program managers can identify investment opportunities rapidly and researchers can more easily build on existing knowledge rather than redoing things from scratch.

Speaker:

Dr. Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service.  She is founder or co-founder of many data initiatives that have served the public good, including the Longitudinal-Employer Household Dynamics Program at the Census Bureau; the Star Metrics/UMETRICS program that led to the establishment of the Institute for Research on Innovation and Science at the University of Michigan; the New Zealand Integrated Data Infrastructure, which holds data from across various sectors; the NORC Data Enclave supporting research access to confidential data; the Patentsview project to increase the usability of patent data; and the Coleridge Initiative to use data more effectively in government decision-making. She currently serves on the Advisory Committee on Data for Evidence Building and the National AI Research Resources Task Force. Her most recent paper(link is external) was published in Nature, and used UMETRICS data.

October’s Containers and Workflow Interest Group (CWIG) webinar series.

Presenters:
Mike Callaghan, Cloud Customer Experience Account Lead, Google
Dave Belardo, Customer Engineer, Google
Mike Callaghan, Customer Engineer, Deloitte Consulting

Meeting number (access code): 2318 931 3839

Meeting password: YQbjmZ3U32@

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study.

This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class.

What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series.

What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series.

Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.

Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

This is the first course in a four-part series on data visualization in R. A basic understanding of R is expected. This class provides a basic overview using ggplot, which is a part of the tidyverse. The tidyverse is a collection of R packages designed for data science. Participants are encouraged to install R and RStudio before the course so that they can follow along with the instructor. Participants will need to download the class data before the webinar. Separate registration is required for each course in the series.

This seminar series showcases research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel Report. These presentations will inform the community about the progress of Cancer Moonshot–funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Presenter:

Speaker: Zeynep Gümüş Ph.D., Icahn School of Medicine at Mt. Sinai

In this hands-on virtual lab, the participants will familiarize themselves with Deep Learning concepts and techniques, using MATLAB Online to train deep neural networks on GPUs in the cloud, create deep learning models from scratch for images and signal data, explore pretrained models and use transfer learning, import and export models from Python frameworks such as Keras and PyTorch, as well as automatically generate code for embedded targets. Deep Learning can achieve state-of-the-art accuracy when it comes to complex problems such as image classification or developing predictive models for signal processing applications. Deep Learning outperforms humans in some tasks like classifying objects in images. Cancer researchers are using deep learning to automatically detect cancer cells, for example.  This is an introductory class with a 3.5 hour duration, including a 20 minute break.

This seminar series showcases research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel Report. These presentations will inform the community about the progress of Cancer Moonshot–funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Speaker: Heidi Hanson, Ph.D., Oak Ridge National Laboratory