After processing the data with GoMiner (http://discover.nci.nih.gov/gominer)the summary export (.se) and categorygenes export files (.gce) have to be exported via the middle panel of the GoMiner graphical user interface (GUI). The desired root category node needs to be selected by a single left click, and the context menu activated by a single right click. The two menu items
should be selected this way to save the .se and .gce files to disk.
Optimization and visualization parameters can be configured via the parameter dialog (menu Options/Options).
size factor (default 1.0) 
Can be adapted to grow/shrink the polygons to fit better in the bounding box which may occur if the intersections between sets is very small (so the diagram is too wide for the bounding box  reduce e.g. to 0.7 in those cases). 
number of edges (default 16) 
How many edges should the polygons have (>=3) 
Seed (default 173) 
The random seed value ensures reproducible results (for one software version). Other solutions are found if this value is changed. For negative values the current system time is used as random seed. 
Update interval (default 10) 
The number of optimization steps after which the currently best solution is shown while the optimization is running. 
Max categories (default 10) 
Exceeding the maximum number of categories shows a warning message. Raise this value to suppress warnings. Higher number of categories may need more computational power. 
Log2 #elements  Activate this checkbox to transform the number of elements to logarithmic scale. For consistency we set log(0)→log(1)→log(2)→1. 
Color mode on  Toggle color mode on/off. 
error function type (default 1) 
0=old error function, 1=new error function 
max intersections (default 6) 
The maximum number of sets which will observed when evaluating the error function 
alpha (default 10.0) 
Error function weight for unwanted intersection 
beta (default 20.0) 
Error function weight for missing intersections 
gamma (default 5.0) 

delta (default 400.0) 
Pressure term for more compact solutions 
min scale (default 1.0) 
Allow changes of the polygon radii (experimental). 
max scale (default 1.0) 
Allow changes of the polygon radii (experimental). 
Different optimizers can be chosen in the Optimizer panel (The Evolutionary newis experimental).
The mutation parameters (like min mutation and max mutation) behave relative to the bounding box where the individuals "live". So a mutation rate of 0.2 can move an polygon 1/5th of the total bounding box.
tau (default 1.0) 
Mutation basis parameter (mutation of the mutation parameter) 
min mutation (default 0.01) 
Lower bound for the mutation rate 
max mutation (default 0.2) 
Upper bound for the mutation rate 
generation size (default 30) 
How many individuals are in the generation (25100 individuals are reasonable). 
clone fraction (default 0.2) 
The best individual has N=clone fraction * generation size outcomes. The individual with rank r has at most min(1,N/r) outcomes. 
max opt steps (default 200) 
Maximum number of optimization steps. Increase this value for better solutions. 
max const steps (default 25) 
Stopping condition for the evolutionary algorithm. If there is no cost improvement in the best individual within max const steps epochs the optimization will be stopped. Increase this value for better solutions. 
numParticles (default 30) 
How many particles are in the swarm (25100 individuals are reasonable). Each particle represents a solution to the problem. 
cGlobal (default 0.5) 
The velocity of each individual is influenced by the global optimum (that is: the best individual to its best iteration up to now). 
cLocal (default 0.5) 
The velocity of each individual is influenced by the local optimum (that is: the optimum of the respective individual up to now). 
maxIterations (default 200) 
Maximum number of optimization steps. Increase this value for better solutions. 
maxConstIterations (default 25) 
Stopping condition for the evolutionary algorithm. If there is no cost improvement in the globally best particle within maxConstIterations epochs the optimization will be stopped. Increase this value for better solutions. 
reflect (default true) 
If reflect is checked the particles are reflected at the bounding box (the domain of the optimization problem) resulting in an inverted velocity vector in the corresponding coordinates. 
This optimizer is the default optimizer. It's the parallel version of the PSO and uses the same parameters. It will dynamically chose the amount of parallel threads to use based on the number of processor cores available on the machine.
The functionality of VennMaster can be automated from the command line to enable batch processing of multiple files. Type the following call in your VennMaster installation directory:
java Xms256m Xmx256m jar venn.jar arguments ...
The two Xoptions are necessary to adapt the memory assignment to VennMaster. For larger problems it may be necessary to increase those values (e.g. to 512).
The original version of this page is available at
http://www.informatik.uniulm.de/ni/staff/HKestler/vennm/doc.html.
Our paper "Big Data and Precision Medicine  Challenges and Strategies with Health Care Data" has been published in Journal of Data Science and Analytics.
Our paper "Stability of Signaling Pathways during Aging  A Boolean Network Approach" has been published in MDPI Biology.