Qlucore

Qlucore began as a collaborative research project at Lund University, Sweden, with researchers in the Departments of Mathematics and Clinical Genetics.From the beginning, the main problem the project faced was the vast amount of high-dimensional data generated by microarray gene expression analysis. As a result, it became clear that an interactive software tool was needed to conceptualize this information.The basic concept behind the Qlucore software is to provide a tool which can take full advantage of the most powerful pattern recognizer that exists - the human brain. The result is a core software engine that visualizes the data in 3D and which can therefore help the user to identify hidden structures and patterns.Over the last fifteen years, a major effort has been made to build upon these early ideas and to develop a core software engine which is extremely fast and allows the user to explore and analyze high-dimensional data sets - interactively and in real time - with the use of a normal computer. The generic software engine has a user interface and a user model optimized for fast and easy use which has been developed and for many types of data it now only requires a very limited number of key presses or mouse clicks to generate the first plot with relevant content.Qlucore was founded in early 2007. Since then, a new version of the data analysis software Qlucore Omics Explorer has been released approximately every nine months. In 2016 we added machine learning functioanlity and in 2017 we took a major step in technology by adding the NGS module including a dynamic and fast Genome browser to enable analysis of data being generated with Next Generation Sequencing (NGS) technologies.Precision medicine is going to have a fundamental impact on healthcare and in 2020 the company broadened it's offering by adding solutions for precision and companion diagnostics. The solutions are built on a software platform for multi-omics companion and precision diagnostics including AI-powered, disease-specific machine learning-based classifier models are combined with patient-friendly visualizations. The models are added as plug-ins to the sclabale and flexible platform that includes report generation functionality. It is shaped for the future of precision oncology and will enable the clinics to implement the latest cancer detection & analysis models.