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BiologicalNetworks 1.9 (current)

10 Dec 2007

New features added:

  • Finding associations among pathways, expression patterns and functional annotations
    • Visualizing, filtering and exploring associations
    • Finding biological significance of associations
  • Co-occurence discovery:
    • Discovering significant annotations enriched in a gene list and microarray experiment
    • Discovering significant annotations enriched in a microarray experiment and curated pathways
  • Import/export
    • Improved curated pathways import
    • Improved SBML support
  • Clustering nodes in network based on GO annotations
  • Search for genes and proteins in curated pathways
  • Tested on Java 5 and 6
Download v1.9

BiologicalNetworks 1.5

18 May 2007

New features added:

  • SBML support
  • Save complete BNProject
  • Open model from archive file
  • New Visual Mapper:
    • Node visual properties customization
      • Set node color, border color
      • Set tumbnail
    • Node visual properties customization
      • Set color, width and stroke for individual edge
      • Set color, width and stroke for all edge of specified type
  • Works on Windows and Mac OS X
  • Tested on Java 5

BiologicalNetworks 1.0

04 Apr 2006

  • Build pathways
    • Expand pathway
    • Use filtering
    • Find shortest path between two genes or all shortest paths covering group of genes
    • Find only direct interactions
    • Find common targets or find common regulators
    • Find intersections with known curated pathways
  • Explore pathways
    • Move, drag and drop biological components on the workspace
    • Cut, Copy, Paste and Delete biological components
    • Different layout algorithms
    • ZoomIn/ZoomOut/ZoomToFit
    • Variety of preloaded curated common pathways
  • Supported file formats: SIF, GML, XML, tab delimited
  • Network Analysis and Statistics
    • Network statistics
    • Finding cycles
    • Pathway Homology
    • Node and Connector Tables
  • Microarray Gene Expression Data
    • Import microarray gene expression data sets
    • Filter, Normalize, make Data transformations
    • Different clustering methods
    • Derive microarray expression correlation networks
    • Overlay expression data onto an open network
    • Sort and search over your expression data

 
     
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