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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

Job Openings

Wissenschaftlicher Mitarbeiter (m/w/d) 

 

Latest News

 

  1. Our paper "Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer-Results of the prospective multicenter PERMAD trial" has been published in PLoS One.

     

  2. "Introducing a machine learning algorithm for delirium prediction—the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead)has been published in Age and Ageing.

Our paper "Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio" has been published in Scientific Reports.

 

"Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study" has been published in Neurological Research and Practice.

 

Our paper "GatekeepR: an R shiny application for the identification of nodes with high dynamic impact in boolean networks" has been published online first in Bioinformatics.

 

Our paper "The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices" has been published in JMIR Medical Informatics.

 

"Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients" has been published in npj Precision Oncology.

 

Our paper "AMBAR-interactive alteration annotations for molecular tumor boards" has been published in Computer Methods and Programs in Biomedicine.

 

"A protocol for the use of cloud-based quantum computers for logical network analysis of biological systems" has been published in STAR Protocols.

 

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.