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

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain.[…]

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Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship[…]

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Biological network topology features predict gene dependencies in cancer cell lines

In this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene[…]

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