Biological Applications

Our company is very interested in developing software for biological applications.

Accumulation of vast biological data in genomics, proteomics, transcriptomics, climate change studies, drug discovery, crop improvement etc. forces finding solutions for efficient data processing, analysis and visualization of results.  We are ready to provide with new efficient data management techniques that will promote better decision making and help in greater innovations. We contribute to the applications in several directions.

Automatic analysis of biological objects based on computer vision

A high-throughput method was developed by the NeuroCorn UG in 2019.

Our goal was to establish an advanced tool for counting and size measurements of small plant objects like embryos or pollen. To achieve this goal we used an image processing technology based on the machine learning library TensorFlow (Google) and Keras. The advantages of the method are its high performance, high reproducibility, rapidity, the possibility of mathematical processing of the results and their visualization. The developed program can accurately measure objects of different size and shape on the same image. Due to the application of machine learning, it allows precise recognition of the touched objects and their counting. The program works with images of different quality and automatically adjust parameters for successful image processing.

Biological Big Data Analytics (omics)

We are a team of experienced scientists with specific expertise in omics analytics and the programmers with advanced skills in programming and machine learning automation. We provide personalized customization and support with experimental design and bioinformatics analyses. We process biologically relevant information by using a variety of state-of-the art tools. Our aim is to summarize, visualize, interpret complex data and present the results in a user-friendly format. At the same time, we are always open to develop specific pipelines to support innovative ideas of customers.