Press release
Natera Launches Proprietary AI Foundation Models to Accelerate Diagnostic and Therapeutic Innovation
Proprietary platform is built on one of the largest multimodal oncology datasets, including 250,000+ tumor exomes, 1 million+ plasma timepoints, and enriched
About this update from Natera, Inc.
[{"type":"text","content":"\nProprietary platform is built on one of the largest multimodal oncology datasets, including 250,000+ tumor exomes, 1 million+ plasma timepoints, and enriched clinical and imaging records\n\n\n AUSTIN, Texas--(BUSINESS WIRE)--\nNatera, Inc. (NASDAQ: NTRA), a global leader in cell-free DNA and precision medicine, today announced the launch of its proprietary AI foundation model platform. These models and applications, which were developed in-house, are designed to drive innovation across therapeutic development, from early target discovery to real-time clinical decision support. The platform features a modular, multimodal architecture composed of three integrated layers:\n\n\n\nData foundation layer\nAt its core is one of the largest longitudinal, multimodal oncology datasets ever compiled, purpose-built for AI training. This includes de-identified data from more than 250,000 tumor exomes and over 1 million longitudinal plasma timepoints, enriched with clinical records, treatment histories, digital imaging, expression profiling and outcomes data. This high-fidelity dataset enables scalable, AI-ready model development.\n\n\n\n\n\nCore model layer\nLeveraging over 1 billion parameters, the core AI foundation model is trained on de-identified Signatera™ and Altera™ datasets and integrates genomic, clinical, and imaging modalities. It is designed to power discovery in multiple domains, including biomarker development, patient stratification and therapeutic response prediction.\n\n\n\n\n\nApplication layer\nSeveral AI-driven applications support clinical decision-making and drug development:\n\n\n\n\nDigital Patient Simulator: Virtually simulates patients for treatment optimization, such as suggesting next-line therapies and de-escalation opportunities. It can also predict patient outcomes.\n\n\n\nReal-Time Trial Matching: Uses molecular and clinical data to identify trial-eligible patients and accelerate recruitment. This engine can also simulate virtual trials to optimize study design and reduce development risk.\n\n\n\nNeoPredict: An advanced algorithm that predicts individual immunotherapy responses based on tumor genomics and neoantigen presentation.\n\n\n\nTwo recent pilot programs of the digital patient simulator demonstrated excellent performance, supporting its potential to enhance clinical decision making in oncology. Resul...