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
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.[…]
Read moreConnecting 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[…]
Read moreBiological 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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