• DOI:10.48550/arXiv.2210.04597
• Corpus ID: 252781327

DeepVenn - a web application for the creation of area-proportional Venn diagrams using the deep learning framework Tensorflow.js

@article{Hulsen2022DeepVennA,
  title={DeepVenn - a web application for the creation of area-proportional Venn diagrams using the deep learning framework Tensorflow.js},
  author={Tim Hulsen},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.04597}
}

• Tim Hulsen
• Published 27 September 2022
• Computer Science
• ArXiv

Motivation: The Venn diagram is one of the most popular methods to visualize the overlap and dif-ferences 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 Some existing solutions also have limited accuracy because of outdated algorithms to calculate the optimal placement of the circles.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.