Classes & Events

Investigating the Impact of Silencers on Disease using Deep Learning

This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.  

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

• Recognize four freely available IDEs for python coding
• Identify fundamental components of python code
• Understand how and why notebooks support rigor and transparency in analysis 

Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.  

If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option. 

This talk delves into the innovative utilization of generative AI in propelling biomedical research forward. By harnessing single-cell sequencing data, we developed scGPT, a foundational model that extracts biological insights from an extensive dataset of over 33 million cells. Analogous to how words form text, genes define cells, effectively bridging the technological and biological realms. The strategic application of scGPT via transfer learning significantly boosts its efficacy in diverse applications such as cell-type annotation, multi-batch integration, and gene network inference.

Additionally, the talk will spotlight MedSAM, a state-of-the-art segmentation foundational model. Designed for universal application, MedSAM excels across various medical imaging tasks and modalities. It showcased unprecedented advancements in 30 segmentation tasks, outperforming existing models considerably. Notably, MedSAM possesses the unique ability for zero-shot and few-shot segmentation, enabling it to identify previously unseen tumor types and swiftly adapt to novel imaging modalities. Collectively, these breakthroughs emphasize the importance of developing versatile and efficient foundational models. These models are poised to address the expanding needs of imaging and omics data, thus driving continuous innovation in biomedical analysis.

Qlucore Omics Explorer is a point-and-click software that enables analysis of RNA sequencing (bulk and single cell), proteomics and metabolomics data. It’s machine learning capabilities allow for cell type classification. This software is available to NCI CCR scientists. Submit a ticket at https://service.cancer.gov/ncisp to get it installed. This session covering bulk RNA sequencing introduces participants to experimental design, data import, normalization, differential expression analysis, visualizations, and biological interpretation (i.e. GSEA, pathway visualization, biological networks, GO enrichment). Experience using or installation of this software is not required for attendance. This class is a demonstration and not hands-on. Participation is restricted to NIH staff. Meeting link will be provided upon approval of registration.

This 45-minute online Lunch and Learn training will help attendees develop their own customized strategy for responsibly incorporating generative artificial intelligence (AI) tools, such as ChatGPT, into their workflows. 

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

• Assess appropriate use cases for generative AI tools within their specific research/work context 

• Develop a customized generative AI usage strategy 

• Document their approach for using generative AI tools 

Attendees are not expected to have any prior knowledge of generative AI tools to be successful in this training. 

Replicability in Biomedicine: Challenges, Causes and Corrections

In this hour and a half online training, attendees will be presented with simple ways to improve and optimize their code that can boost execution speed by orders of magnitude. Attendees will also learn about common pitfalls in writing MATLAB code, explore the use of the MATLAB Profiler to find bottlenecks, and will be introduced to the use of Parallel Computing Toolbox and MATLAB Parallel Server to solve computationally and data-intensive problems on GPUs, multicore computers, clusters and cloud platforms (e.g. AWS, Azure, etc).

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

• Understanding vectorization and best coding practices in MATLAB
• Addressing bottlenecks in your programs
• Incorporating compiled languages, such as C, into your MATLAB applications
• Utilizing additional hardware, including multicore processors and GPUS, to improve performance
• Scaling up to a computer cluster, grid environment or cloud

Attendees are expected to have some prior knowledge of MATLAB. This training is taught by MathWorks. Installation for MATLAB is not needed.  

This 30-minute online training provides a high-level overview of recent developments in artificial intelligence (AI). Each session highlights emerging trends, tools, and use cases in the evolving AI landscape, with an emphasis on practical relevance and responsible use. Whether you're just getting started or looking to stay current, this training offers timely insights in a concise format.  

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

• Summarize key trends and developments in AI 

• Identify new tools, capabilities, or applications relevant to their work 

• Describe considerations for ethical and responsible use of AI technologies 

Attendees are not expected to have any prior knowledge to be successful in this training. 

Simple Questions Your RAG System Can't Answer

Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA-seq, proteomics, metabolomics, as well as enabling machine learning classification of cell types. Submit a ticket at https://service.cancer.gov/ncisp to get it installed. In this session, participants will learn to apply regression approaches to identify correlation between bulk RNA and protein expression using this software. Experience using or installation of Qlucore Omics Explorer is not needed to attend. Attendance is restricted to NIH staff. Meeting link will be provided upon approval of registration.

This one-hour online training offers an overview of the NIH-sponsored Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo), and the role of participating in these repositories in the NIH data repository landscape for intramural researchers. The session will highlight how these repositories support compliance with the NIH Data Management and Sharing Policy.

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

• Describe how generalist repositories fit into the NIH data repository landscape for intramural researchers.
• Understand how these repositories support compliance with the NIH Data Management and Sharing Policy
• Learn about the resources developed by GREI repositories to support data sharing workflows, including a generalist repository comparison chart, a generalist repository selection flowchart, a data submission checklist, and a data management and sharing plan guide.
• Gain practical insights from real-world examples, demonstrating  how researchers use generalist repositories for data sharing and reuse, and how these efforts contribute to the broader NIH data sharing ecosystem.

Attendees are not expected to have any prior knowledge of the NIH Data Repository Landscape.    

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. This class focuses on bulk RNA sequencing analysis where Partek scientist will teach participants how to start from FASTQ files and obtain differential expression analysis results, create visualizations, and extract biological insight through pathway analysis. This class is a demonstration and not hands-on. Experience using or access to Partek Flow is not required to participate. Attendance is restricted to NIH staff.

The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. In this talk, Dr. Pollard will share examples from human genetics, where they have built deep learning models of 3D chromatin interactions that take only sequence as input and then used them to interpret disease variants. This strategy leads to causal hypotheses and enables them to prioritize variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in cancer and developmental disorders. This prediction-first strategy exemplifies Dr. Pollard's vision for a more proactive, rather than reactive, role for computational science in biomedical research.

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating visualizations for single cell RNA sequencing data. This session is a demonstration and not hands-on. Experience using or access to Partek Flow is not needed for participation. Attendance is restricted to NIH staff.