ImJoy
ImJoy enables execution and deployment of deep learning models and plugins for biomedical image analysis and genomics.
Key Features:
- Deep Learning Plugins: Support for a variety of plugins that run deep learning models for biomedical analyses.
- Mobile and Interactive Image Analysis: Specialized plugins provide interactive image analysis workflows that can operate in mobile or interactive contexts.
- Genomics Analysis: Plugins facilitate deep-learning-based analysis of genetic data, including sequence analysis, variant calling, and functional annotation.
- Plugin Deployment and Development: Capability to deploy pre-built solutions or develop custom plugins to integrate and run computational models.
Scientific Applications:
- Biomedical image analysis: Application of deep learning models to analyze and interpret biological and microscopy images.
- Sequence analysis: Use of deep learning approaches for analyzing nucleotide sequence data.
- Variant calling and functional annotation: Deep-learning-enabled workflows for identifying genetic variants and annotating their potential functions.
Methodology:
Execution of deep learning models via plugins to perform image analysis and genomics tasks, including sequence analysis, variant calling, and functional annotation.
Topics
Details
- Tool Type:
- web application
- Added:
- 1/14/2020
- Last Updated:
- 4/16/2021
Operations
Data Inputs & Outputs
Publications
Ouyang W, Mueller F, Hjelmare M, Lundberg E, Zimmer C. ImJoy: an open-source computational platform for the deep learning era. Nature Methods. 2019;16(12):1199-1200. doi:10.1038/s41592-019-0627-0. PMID:31780825.
PMID: 31780825
Funding: - Fondation pour la Recherche Médicale: DEQ 20150331762
- Knut och Alice Wallenbergs Stiftelse: 2016.0204
- Familjen Erling-Perssons Stiftelse: 20180316
- Vetenskapsrådet: 2017-05327
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd: 2017-0532
Documentation
User manual
https://imjoy.io/docs