machine learning under your control

Powerful, flexible, open-source and easy to use. Home for all your knowledge discovery questions

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

FEATURE LEARNING

You can discover relevant trends and patterns with ease, that would usually take years of manual handcrafting.

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 forums

Help with SIMON development!

Contribute code, make translations,
fix & report bugs, participate in development

get started

Gather. Train. Predict.

SIMON runs on almost every linux server. Your own instance is only a few mouse clicks away!

get started
WHATS 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

Join us on forums!

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 more

Semi-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 more

Machine 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[…]

Read more

Contact us