We are convinced that an increasing level of integration between hardware and software technologies is important to create an efficient, streamlined workflow for the end users. We are always open to discuss potential technology integrations or joint research opportunities with instrument vendors that share our vision.
Aspect Analytics has strong scientific roots, as evidenced by our scientific publications. Our core expertise encompasses bioinformatics, machine learning and mass spectrometry. If you are looking for a research partner, feel free to let us know.
Via integration with our software, your customers can get seamless access to all of our platform features.
We believe in an integrated future in which end users can perform complex workflows ranging from sample preparation up to data analysis and reporting in a fully streamlined way. As such, we are always interested in dovetailing our software platform with visionary hardware systems.
Expand your offering
Fill in potential gaps in your solution. A collaboration enables you to provide your customers with access to best-in-class bioinformatics tools, as well as a full-fledged data management platform.
Empowering the end user
Native integrations between key components simplify workflows for the end user. We are convinced that the sum of complementary hard -and software technologies can be greater than its parts.
In case of high complementarity of our technologies and a shared vision on how to move forward, we are open to investigating potential strategic partnerships to strengthen both parties involved.
Joint research with Aspect Analytics
As a KU Leuven University spin-off, Aspect Analytics has strong scientific roots, as evidenced by our scientific publications. We remain active in cutting-edge research, with our core expertise encompassing bioinformatics, machine learning and mass spectrometry.
We have broad experience analysing biological data, with a primary focus on mass spectrometry imaging and microscopy data analysis. We are currently actively researching techniques for true multimodal data fusion across various imaging and non-imaging modalities.
Our machine learning expertise includes deep learning, hyperparameter optimization, building and evaluating predictive models in an unsupervised context, as well as Bayesian inference.
We have a long-standing research history in mass spectrometry imaging, including a recently published review paper together with prof. Caprioli. In addition to MSI, we are increasingly involved in analysis of complex LC-MS data.