OPEN SOURCE
genular is a community behind SIMON an open source automated machine learning software, built by a vibrant community of people like you!
VISUALIZE & ANALYZE
Exploratory analysis of machine learning results with help of many different visualization algorithms will give you instant insights into your questions
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How to get started?
Getting started is easy! Join our community and help us make science more open and better!
Join the genular Community
Join us on #genular on freenode.net Find us on Twitter under @genular or join us on forums
community forumsHelp with SIMON development!
Contribute code, make translations,
fix & report bugs, participate in development
Gather. Train. Predict.
SIMON runs on almost every linux server. Your own instance is only a few mouse clicks away!
get startedWHATS ALL THIS ABOUT?
SIMON is a powerful, flexible, open-source and easy to use knowledge discovery application. Currently SIMON implements automated machine learning and statistical data discovery features that will help you to easily illustrate dynamic relationships and provide you with a structural sense of your data.
CHECK OUT DEMO
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Project Maintainers
genular is created and supported by a open source community of users, developers and enthusiasts from around the globe.
We are always welcoming you to join us and make scientific software more accessible to scientists and users across the globe!
Ivan
LogIN
Project founder, system architecture. Backend and frontend design.
Adriana
atomic
Project founder, fixes & improvements all over the place, community
Latest news from us
Please find latest announcements in our blog below
Deep functional synthesis: a machine learning approach to gene functional enrichment
Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present[…]
Read moreSemi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy
2-photon intravital microscopy (2P-IVM) is a key technique to investigate cell migration and cell-to-cell interactions in organs and tissues of living organisms. Focusing on immunology, 2P-IVM allowed recording videos of[…]
Read moreMachine learning of stochastic gene network phenotypes
A recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics[…]
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