Imagine your body's genetic instructions are like a giant book, and cancer is a series of typos in that book. This software acts like a high-speed spell-checker that finds those typos and tells doctors exactly which type of cancer it is. By identifying these patterns, doctors can pick the right medicine for the specific version of the disease a patient has.
Traditional cancer diagnostics often rely on single biomarkers, which fail to capture the full complexity of tumor heterogeneity. This leads to less accurate diagnoses and suboptimal treatment choices for patients.
AI-powered software for RNA-seq analysis that classifies AML and bladder cancer into clinically relevant subtypes. It includes machine-learning models for gene expression signatures and gene fusion analysis.
If you are a diagnostic lab dealing with slow and manual RNA-seq analysis — this project developed AI-powered classification models that provide more accurate diagnosis and prognosis for AML and bladder cancer. This allows for faster turnaround of results for patients.
If you are a biotech company dealing with the need for precise patient selection in clinical trials — this project developed companion diagnostic software that identifies specific gene expression signatures. This ensures the right patients receive the right targeted therapy.
If you are a health IT provider dealing with the integration of complex genomic data into clinical workflows — this project developed IVDR-approved software solutions that simplify the interpretation of RNA-seq data. This speeds up the adoption of new clinical guidelines in hospitals.
Based on available project data, specific pricing or cost details for the commercial software are not provided.
Yes, the objective is to scale up into non-cancer indications such as cardiovascular, neurological diseases, and immunological disorders after covering relevant cancer types.
Based on available project data, specific licensing terms are not mentioned, but the project is led by QLUCORE AB, an SME developing commercial software solutions.
The project focuses on developing and CE-certifying classification models under the IVDR (In Vitro Diagnostic Regulation) framework.
The project period runs from 2024-05-01 to 2027-04-30.
The project is managed by a single-partner consortium consisting of QLUCORE AB, a Swedish SME. This 100% industry-led structure indicates a strong focus on commercialization and rapid market entry, although they collaborate closely with research groups at Lund University for data generation.
Contact QLUCORE AB in Sweden regarding their IVDR-approved AI diagnostics.
Contact us to find similar AI-driven diagnostic opportunities in the EU.
