If you have an idea that a dedicated group of scientists and programmers could help you achieve in a few days of concentrated work, please apply at this form. The general application form for hackathon participants will be available here soon.
Please see the team leader requirements below to ensure you know what to expect and what will be expected of a team leader.
A team leader is responsible for proposing a project for the event and having a clear vision for developing a solution. To accomplish their goal the team leader is responsible for leading their team of 5-6 individuals at the event - this means clearly defining and delegating tasks, incorporating team members’ ideas to accomplish the goal at stake, and ensuring success of the team. Great team leaders will understand the science involved in their project and, if a software prototype is involved, will also be able to manage its development. Not all projects have to be software oriented - anything that might benefit from the efforts of a group of talented people collaborating is welcome.
Important points to consider:
The software you utilize, integrate, or produce must be open source or at least open-use and your source should be kept on GitHub.
A manuscript, white paper, or other publication is encouraged from each team.
Team leaders are responsible for delegating tasks to their team members and ensuring the proposed project gets completed.
This year, special consideration will be given to projects that emphasize data visualization.
Current Projects accepted for 2019 Hackathon
Rob Schaefer (Linkage Analytics)
COB: the co-expression browser
COB is a open-source package built to browse gene co-expression networks created by Camoco (https://github.com/LinkageIO/Camoco), a python package used to build and integrate genome-wide association studies with gene co-expression data. Using COB, users are able to interactively browse and visualize GWAS data and gene co-expression networks either locally on their own machines or through the web.
- Synchronize COB with latest version of Camoco (v0.6.2+) - Streamline COB installation process - Improve software speed/performance - Implement a visualization for genomic features (possibly using JBrowse) - Implement full network browsing functionality - requires dynamic node and edge loading of network based on zoom level - Upgrade to latest version of cytoscape-js
Dina Mikdadi (Deloitte)
Alzheimer’s Disease Use Case with Machine Learning
We plan to collect multiple NIAGADS of heterogenous platforms and other necessary datasets from different repositories. If granted accesses to the data sets we need, we will use deep learning and ensemble approaches to isolate genomic variants that have been identified, matched gene expression levels with genotypes and phenotypes aligned to genotypes for AD.