Events by this organizer
Artificial Intelligence / Machine Learning
Natural Language Processing
NCI Genomic Data Commons
NIH High Performance Unix Cluster Biowulf
Single Cell RNA SEQ
Single Cell Technologies
CCR Neuro-Oncology Branch
Data Science Learning Exchange
Earl Stadtman Investigator Program
FNL Science and Technology Group
Frederick National Lab for Cancer Research (FNLCR), Advanced Biomedical Computational Science (ABCS)
NCI and the Society for Immunotherapy of Cancer (SITC)
NCI CCR Liver Cancer Program
NCI Containers and Workflows Interest Group
NCI Data Science Learning Exchange
NHLBI Proteomics Core
NIH Common Fund
NIH HPC Biowulf
NIH Metabolomics Scientific Interest Group
NIH Office of Data Science Strategy (ODSS)
NIH STRIDES Initiative
NIH Training Library
Office of Cancer Clinical Proteomics Research
Scientific Library at Frederick
Single Cell Users Group
Systems Biology Interest Group
Past & Future Events
Only Past Events
Only Future Events
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
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
(Thursday) 1:00 pm - 2:00 pm
Register/Join This webinar will focus on making controlled-access data (stored in NIH operated and supported
This webinar will focus on making controlled-access data (stored in NIH operated and supported repositories) more readily findable and accessible. It will consider the benefits of standardized vocabularies to address and describe a data set’s contents and a common language for informed consent that allows for consistent interpretation of allowable data uses. Discussion points will also address current issues with access to summary data and how best to make summary data and metadata available and accessible.
This webinar is a breakout session from the July 9 webinar, Streamlining Access to Controlled Data at NIH: Tackling Challenges and Identifying Opportunities. To learn more about this topic, including additional breakout sessions planned for July 2021, visit the Office of Data Science Strategy webpage
(Thursday) 3:00 pm - 5:30 pm
Register/Join During this month's NCI Cancer Research Data Commons (CRDC) Cancer Genomics Cloud (CGC) webinar series, two Georgetown University staff members about leveraging the CGC in their
During this month’s NCI Cancer Research Data Commons (CRDC) Cancer Genomics Cloud (CGC) webinar series, two Georgetown University staff members about leveraging the CGC in their data science courses and curriculum.
Building from their experience as trainers in the fields of bioinformatics and computational biology, Dr. Yuriy Gusev and Ms. Krithika Bhuvaneshwar will cover:
- approach and methodology for establishing their online data science course “Demystifying Big Biomedical Data: A User’s guide.”
- examples of graduate-level courses that leverage the CGC as a teaching platform in the Masters in Health Informatics and Data Science program at Georgetown University.
As one of the three Cloud Resources within the NCI CRDC, the CGC provides researchers access to a wide variety of data sets, a catalog of tools to analyze and visualize the data directly from the browser, and scalable computational resources to perform large-scale analysis on the cloud.
- Yuriy Gusev, Ph.D.
Dr. Yuriy Gusev is an associate professor of bioinformatics and a bioinformatics Lead at the Georgetown University Innovation Center for Biomedical Informatics. Dr. Gusev is the director of the graduate program for a Masters in Health Informatics and Data Science and co-director of the informatics shared resource for the Lombardi Comprehensive Cancer Center at Georgetown University. He has over 20 years of experience in teaching and training in bioinformatics and computational biology at several academic centers in the U.S. He has developed several new curricula for graduate and undergraduate programs at Georgetown. He has also developed a successful massive open online course on EdX titled, “Demystifying Big Biomedical Data: A User’s Guide,” which attracted over 8,000 students from around the world.
- Krithika Bhuvaneshwar
Ms. Krithika Bhuvaneshwar is a research instructor faculty and curriculum coordinator for the Masters in Health Informatics and Data Science program and is also a Senior Bioinformatician at the Innovation Center for Biomedical Informatics, Georgetown University. She has helped organize training workshops in Elsevier Pathway Studio, Globus Genomics, systems biology, immuno-oncology, and — most recently — imaging informatics for faculty and staff at Georgetown University Medical Center.
(Wednesday) 2:00 pm - 3:00 pm
23sep1:00 pm2:00 pmBuilding predictive model from multimodal data using machine learningAI for multimodal data presented by members of the Strategic Data Science Initiative, Frederick National Laboratory for Cancer Research.
Registration: https://btep.ccr.cancer.gov/classes/ai_four/ Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m5fa0e43ae167ed5ea3a77fb25d339a82 Description: In this talk, we will highlight two examples for building predictive models from multi modal data. The first example predicts dose response in cell
Description: In this talk, we will highlight two examples for building predictive models from multi modal data. The first example predicts dose response in cell lines based on drug and molecular features. The second example will show to combine pathology whole slide images and molecular features for cancer diagnosis and prognosis.
Presenters: George Zaki, Bioinformatics Manager, Strategic and Data Science Initiatives (SDSI), Frederick National Laboratory for Cancer Research (FNL), Pinyi Lu, Bioinformatics analyst, SDSI, FNL
(Thursday) 1:00 pm - 2:00 pm