Ongoing work to comply with the CE-IVDR (clinical use). The program includes classifier models and supportive gene fusion analysis.
AI-powered, disease-specific machine learning classifier models
User-friendly interface for exploration of detected gene fusions, with no need for bioinformatics expertise.
Patient-friendly visualizations in an easy-to-use and cost-effective software solution.
Based on RNA sequencing data and standard NGS workflows.
Acute Lymphoblastic Leukemia Diagnostics
The first application area for the Qlucore Diagnostics platform is RNA-seq based cancer diagnostics in Acute Lymphoblastic Leukemia, including both gene expression subtype classification of B-cell leukemias and gene fusion analysis support.
Work is ongoing to certify Qlucore Diagnostics for Acute Lymphoblastic Leukemia to comply with the IVDR regulation (CE).
Informative and customized reports
The output of Qlucore Diagnostics is highly configurable. The base is a pdf report that includes conclusions, results, plots, quality metrics and method information. The report generator includes a tool for locally customized conclusions. The report content can be customized and adjusted to fit your specific needs.
Visualizations are easily included in the clinical report for better communication between a molecular genetics lab, clinicians and patients.
View an example of the easy-to-interpret diagnostics report below.
Learn more about our different models for precision diagnostics
Two kinds of models for precision diagnostics.
One platform: many applications
Powerful but easy to understand visualizations are easily configured for each application. All powered by one platform. Application examples are: Clinical diagnostics in hospital or central labs, companion-diagnostics driven by pharma companies and new precision medicine therapies and specific software applications for new diagnostics instruments.
The Qlucore Diagnostics solution is built on a flexible and generic platform which is the foundation for a range of precision diagnostics and companion diagnostics applications. Input can be any type of omics data. AI-powered machine learning-based classifiers can be combined with rule-based logic and other types of evaluations such as gene fusion analysis.