Open-Source package for segmentation and analysis of gel electrophoresis images using machine learning. Both a python framework for training/running models and a QuPath GUI extension are available.

A new machine learning model can successfully identify bands from gel electrophoresis images. Gel electrophoresis is a staple of many life science labs but has changed little since the 20th century.

The core principle of gel electrophoresis is simple: biomolecules are suspended within inset wells in a gel matrix, a voltage is applied and charged particles are pushed through the matrix. The size ...

University of Edinburgh scientists have harnessed the power of AI in a new tool that promises to speed up analysis of data from gel electrophoresis experiments. The capacity to regulate the ...

Despite its standalone capabilities, HCR is highly modular; therefore, it can be advanced and repurposed when coupled with latest discoveries. To this effect, we have developed a gel ...

In these sorts of quantitative applications, image analysis offers many advantages.In this paper we present a computerized image analysis of gel electrophoresis. Measurement made on image is more ...

A rapid and sensitive method to detect single-walled carbon nanotubes (SWNTs) in biological samples is presented. The method uses polyacrylamide gel electrophoresis (PAGE) followed by quantification ...