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JANet

JANet (Javascript AgeFactDB Network-viewer) is a specialized 3D Network Exploration and Visualization for Lifespan Data Exploration from AgeFactDB.

 JANet 3D viewer interface screenshot

Features

  • Interactive 3D network visualization (rotate, zoom, etc.)
    • 2D representation
    • Stereo representation (side by side)
    • 3D beamer mode (condensed side by side stereo, converted for shutter glasses)
      JANet 3D beamer mode example
  • Ageing-related network data (AgeFactDB)
    • Lifespan observations
    • Ageing factors
    • Augmentation nodes (e.g.: allele types, citations)
  • Augmentation with external domain knowledge
    • Gene Ontology (GO)
    • KEGG Pathways
  • Genes of interest analysis (e.g.: differentially expressed genes)
  • Scripting language
    • JANet-specific API

Installation

The viewer can be started directly from the server at
https://sysbio.uni-ulm.de/software/janet

or the source code can be downloaded for local installation
(https://sysbio.uni-ulm.de/software/janet/latest.zip).

For a local installation there are two options:

  1. Extract the archive somewhere in the file system.
  2. Install a web server locally and extract the archive somewhere below the server root directory.
  3. Install a neo4j server
  4. python 3.5 or above is required.

Citing JANet

To cite the network viewer in publications, use:


Hühne H, Kessler V, Fürstberger A, Kühlwein S, Platzer M, Sühnel J, Lausser L, Kestler HA. 3D Network exploration and visualisation for lifespan dataBMC Bioinformatics 19(1): 390, 2018. https://doi.org/10.1186/s12859-018-2393-x

Latest News

 

Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU. 

 

Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.

 

The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.

 

Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.

 

Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.

  

Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.

 

We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.