A customizable secure DIY web application for accessing, sharing, and browsing aggregate experimental results and metadata.

Jaewoo LeeMehita AchuthanLucas ChenPaulina Carmona-Mora

Published in: Bioinformatics advances (2024)

The source code of our DIY app is available on https://github.com/Carmona-MoraUCD/Human-Genomics-Browser. It can be downloaded and run by anyone with a web browser, Python3, and Node.js on their machine. The web application is licensed under the MIT license.

Keyphrases

endothelial cells
lymph node
social media
deep learning
single cell
induced pluripotent stem cells
health information
machine learning
pluripotent stem cells