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Class | Date | Time | Location | Training Materials | Registration |
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Intro to O2 | Wednesday, February 21, 2024 | 10am to 12pm | In-person | ||
O2 Portal - Simplifying the Interaction and Experience of Using an HPC Environment | Wednesday, March 6, 2024 | 10am to 12pm | Virtual | ||
Intro to MATLAB | Wednesday, March 13, 2024 | 10am to 12pm | In-person | ||
Intro to Parallel Computing | Wednesday, March 20, 2024 | 10am to 12pm | In-person | ||
Optimizing O2 Jobs | Wednesday, April 3, 2024 | 10am to 12pm | In-person | ||
Intro to Python | Wednesday, April 10, 2024 | 10am to 12pm | In-person | ||
Troubleshooting O2 Jobs | Wednesday, April 24, 2024 | 10am to 12pm | Virtual | ||
Intro to O2 | Wednesday, May 1, 2024 | 10am to 12pm | Virtual | ||
Medical Image Processing and Machine Learning Workflow (Taught by Mathworks) | Wednesday, May 15, 2024 | 10am to 12pm | In-person | ||
RCBio: easy and quick HPC pipeline builder & runner | Wednesday, May 22, 2024 | 10am to 12pm | Virtual |
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Classes sponsored by HMS Research Computing but taught by our partner organizations:
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Overview Medical images are obtained from various sources including MRI, CT, X-ray, ultrasound, and PET scans. Analyzing these images necessitates a comprehensive environment for data access, visualization, processing, and algorithm development. A key challenge involves extracting quantitative features to generate clinically relevant information using advanced techniques like machine learning algorithms. This presentation will explore radiomics, which captures characteristics not typically visible. Radiomics features can be employed across various medical imaging modalities and applications, enabling the study of associations between imaging features and patient biology, as well as the prediction of clinical outcomes, making radiomics a versatile technique in medical imaging. These features quantify shape, intensity, and texture characteristics within medical images, reducing reliance on subjective interpretation for clinical workflows. Highlights In this session, you will learn how to:
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Academic Software Platform (ASP) integrates two major stacks of scientific software: BioGrids stack of ~400 biomedical applications (https://biogrids.org/software/) as well as SBGrid stack of ~500 structural biology applications. Multiple versions of applications are maintained, and users can also easily install the same stack of software on laptops, research workstations or cloud resources, under Linux or Mac OS X operating systems. More information about this resource is available on O2 wiki: https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1630994700/Using+Software+Provided+by+BioGrids |
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