Turing Biosystems develops a software platform that utilizes interpretable AI and automated reasoning to integrate and analyze multimodal clinical and biological data, addressing high failure rates and adverse immune responses in immunotherapy and cell gene therapy. By optimizing clinical outcomes, the platform enables clinicians and biopharma to deliver more effective and safer treatments tailored to individual patient responses.
Funding
Funding not disclosed
Founders
Product
Problem
Immunotherapy and cell & gene therapy face challenges with high failure rates and adverse immune responses. Analyzing patient responses to therapy and designing better treatments is difficult due to the complexity of integrating multimodal clinical and biological data. Current methods struggle to effectively manage this complexity, hindering the development of safer and more effective personalized treatments.
Solution
Turing Biosystems offers a software platform that leverages interpretable AI and automated reasoning to integrate and analyze multimodal clinical and biological data. The platform connects various data types, including metadata, health records, multi-omics, tissue and imaging data, public data, and prior knowledge, to analyze patient responses to therapy and design improved treatments. By focusing on clinical outcomes optimization, the platform aims to enhance the safety and efficacy of therapies for patients, clinicians, and biopharma. The AI provides an evidence chain for its conclusions, empowering scientists and clinicians in their decision-making process.
Target Audience
The primary users are clinicians and biopharmaceutical companies involved in immunotherapy and cell & gene therapy, seeking to improve clinical outcomes and develop more effective, personalized treatments.
Features
- Interpretable AI built on automated reasoning to reproduce human thought processes
- Graph-based integration of multimodal data, including multi-omics, imaging, and clinical records
- AI reasoning audit trail to understand the evidence behind outputs
- Spatial biology module to analyze and simulate multiscale biology
- Bioinformatics R&D tools and pipelines, with support for Nextflow
- Graph engine (TuringDB.ai) for integrating complex biological data
- Integration with existing tools and frameworks, such as Jupyter Notebooks, VS Code, and Neo4J