Want to learn about an explainable biology-informed RNA biomarker?
The white paper presents how Genialis™ krasID predicts treatment responses in KRAS-mutated cancers. Unlike traditional biomarkers that only determine eligibility, Genialis krasID uses RNA data to predict not just whether a patient will respond to KRAS inhibitors, but how effectively they will benefit from the treatment. It addresses the limitations of AI in precision medicine, where models often struggle with the complexity of cancer biology and the high dimensionality of data. By integrating AI with biology-informed models, Genialis krasID offers a deeper understanding of the tumor’s molecular landscape, leading to more reliable predictions. Recognizing that KRAS mutations alone don’t provide a complete picture it takes an integrated approach. It models the diverse aspects of KRAS biology, including signaling pathways and tumor-extrinsic factors, to more accurately identify which patients will benefit from targeted therapies.
Content overview:
- KRAS Inhibitors Need RNA-based Biomarkers
- Biology-Informed Machine Learning for Explaining Complex KRAS-related Tumor Biology
- Genialis™ krasID as a Platform for Custom KRASi Biomarkers
- Starting a Collaboration: See for yourself how well Genialis™ krasID works for your compound
- Clinical Use Case: Validating Genialis™ krasID in Real World Sotorasib-Treated Patients
Enhance your approach to targeted cancer therapies. Download the white paper to explore how Genialis krasID leverages RNA-based predictions to refine treatment strategies for KRAS-mutated cancers. Gain advanced insights that can help you optimize patient outcomes in your research and clinical trials!
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