  Motivation: The Venn diagram is one of the most popular methods to visualize
the overlap and differences between data sets. It is especially useful when it
is are 'area-proportional'; i.e. the sizes of the circles and the overlaps are
proportional to the sizes of the data sets. There are some tools available that
can generate area-proportional Venn Diagrams, but most of them are limited to
two or three circles, and others are not available as a web application or
accept only numbers and not lists of IDs as input. Some existing solutions also
have limited accuracy because of outdated algorithms to calculate the optimal
placement of the circles. The latest machine learning and deep learning
frameworks can offer a solution to this problem. Results: The DeepVenn web
application can create area-proportional Venn diagrams of up to ten sets.
Because of an algorithm implemented with the deep learning framework
Tensorflow.js, DeepVenn automatically finds the optimal solution in which the
overlap between the circles corresponds to the sizes of the overlap as much as
possible. The only required input is two to ten lists of IDs. Optional
parameters include the main title, the subtitle, the set titles and colours of
the circles and the background. The user can choose to display absolute numbers
or percentages in the final diagram. The image can be saved as a PNG file by
right-clicking on it and choosing "Save image as". The right side of the
interface also shows the numbers and contents of all intersections.
Availability: DeepVenn is available at https://www.deepvenn.com. Contact:
<EMAIL_ADDRESS>