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Challenges
St. Jude BioHackathon
Challenges
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Why St. Jude
St. Jude BioHackathon
Challenges
Data Management
Dev Ops and Community
Gui Tool Development
Image Analysis
Processing Pipelines and Methods
Data Management
Simplify patient sample (meta)data tracking and querying
Flexible, customizable, browser-editable persistent storage via databases
Database storage of image data with a unified API
Dev Ops and Community
A platform for connecting the St. Jude community based on research interest and skills
Automated email templating for shared resource facilities at St. Jude
Increasing the reliability and resiliency of the Image Processing Pipeline (IPP) plugin
Scaling CBI image analysis pipeline by leveraging HPC resources
Gui Tool Development
R function for generating shiny apps to visualize Cox proportional hazards regression models
Web app for designing CRISPR gRNAs that utilizes all available orthologues and interactively shows off-target effects of a desired edit
A web app for fine-scale population/ethnicity identification and visualization
An interactive application to perform and interrogate peak calling performance with various parameters
ML dashboard for real-time model building using natural language processing on biological sequence inputs
Proton beam geometry simulation for training, teaching, and optimal treatment geometry
Image Analysis
Automated quantification and classification pipeline for tissue sections and specific morphologies
Add ML-assisted image annotation to napari
Restoration/imputation to improve analysis of low-resolution legacy MRI image data
Processing Pipelines and Methods
Predicting destabilizing point mutations making full use of the structure in AlphaFold DB
Developing a methylation array analysis pipeline
Determine the effects of imputation on ssGSEA/GSVA methods in scRNA-seq
Establishing a workflow for identifying important structural features of a protein of interest integrating information from multiple sources
Toolbox for convenient manipulation of AlphaFold output
A web-based molecular profiler capable of matching ChIP data based on similarity to established profiles
Visualizing spatial transcriptomic data from serial sections in 3-dimensional space
Machine learning pipeline to predict locations of mutations in different cancer types
Visualizing genomic instability in tumors for rapid and robust identification of HRD patients