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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

 

Our paper "A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective" has been published in npj systems biology and applications.

 

"Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician" has been published in PLoS One.

 

"Self-Assessment of Having COVID-19 With the Corona Check Mhealth App" has been published in IEEE Journal of Biomedical and Health Informatics.


Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.

 

Our paper "Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images" has been published in Frontiers in Artificial Intelligence.

 

  1. "The HLA ligandome of oropharyngeal squamous cell carcinomas reveals shared tumour-exclusive peptides for semi-personalised vaccination" has been published online first in the British Journal of Cancer.

 

"Vaccine Side Effects in Health Care Workers after Vaccination against SARS-CoV-2: Data from TüSeRe:exact Study" has been published in Viruses-Basel.

 

"PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults – a study protocol" has been published in BMC Neurology.

 

Our paper "Federated Electronic Data Capture (fEDC): Architecture and Prototype" has been accepted for publiaction in the Journal of Biomedical Informatics.

 

Our paper "Efficient cross-valdation traversals in feature subset selection" has been published in Scientific Reports.

 

Our paper "CANTATA - prediction of missing links in Boolean networks using genetic programming" has been published in Bioinformatics.

 

Our paper "Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives" has been published in the Journal of Medical Internet Research mhealth and uhealth.

 

Our paper "Identification of dynamic driver sets controlling phenotypical landscapes" has been published in the Computational and Structural Biotechnology Journal.