Here you can find some examples of quantitative data analysis performed with our AI-based technologies.
KymoButler is a Deep Neural Network to trace ubiquitous, unidirectional, dynamic processes in kymographs. In our paper (Jakobs et al, 2018), we demonstrate that KymoButler performs equally well or better than manual tracking, and outperforms currently available automated tracking packages. We successfully applied KymoButler to a variety of different kymograph tracing problems.
The network is packaged in a free web-based “one-click” software for use by the wider scientific community. You can buy access to another, more powerful version, through our enquiry form (or just try it out on a couple of images first). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents a step towards the widespread adaptation of Artificial Intelligence techniques in biological data analysis.
You can use KymoButler here: http://kymobutler.deepmirror.ai
Coming Soon – Bidirectional KymoButler
We are working on a version of KymoButler to trace bidirectional dynamic processes in kymographs. This is a more complex task, but as you can see below our network is already getting close to discriminating the traces.
We will make this software available soon!
At deepMirror.ai, we aim to bring state-of-the-art AI-based technology to quantitative data analysis that for too long has relied on manual analysis. By training Deep Neural Networks, we can teach an AI to perform as well as experts.
If your work would benefit from this kind of approach, get in touch with us and we will find the best solution for your specific application.