OUR TECHNOLOGY
Powering Precision Medicine with Multimodal AI
At BioAI, we’re accelerating the discovery of novel biomarkers and drug targets tailored to specific patient profiles, ushering in a new era of precision medicine.
BioAI’s PredictX Platform
The Novel R&D Machine Made With Every Lab in Mind
BioAI’s PredictX Platform intakes your data and generates novel insights. PredictX integrates world-leading AI methodologies, including In-silico phenotype projection and integrated deep learning to map the causal biology of disease, develop digital biomarkers, and identify drug targets.
Combined Inputs
Learn Faster. Accelerate Breakthroughs.
Understanding Causal Biology of Disease with the PredictX Platform
State-of-the-Art AI-based Technologies
In-Silico Phenotype Projection
Our In-silico phenotype projection technology, powered by advanced Causal AI, uncovers dependencies in complex biomedical data, facilitating precise identification of molecular signals and regions in tumor tissues using single-cell RNA-seq and spatial transcriptomics.
This technology enhances biomarker discovery and prediction in digital pathology. It employs an ensemble AI approach, blending deep learning and probabilistic programming, to hypothesize from high-dimensional datasets without prior biological knowledge. This enables the derivation of informative gene interaction networks based on conditional gene state probabilities.
Digital Biomarkers Based on H&E-Stained Tissue
Our technology edge allows BioAI to develop performant and robust H&E-based tests against known biomarkers.
- Known Mutations
- Novel Biomarkers
- Specific Samples: ex. RET, BRAF, CD3
Computational Pathology
BioAI’s team brings decades of professional experience developing and deploying algorithms on histological data for early and clinical drug R&D and diagnostic applications.
Causal AI for Drug Target Discovery
BioAI’s causal AI technology enhances your biopharmaceutical drug discovery process by uncovering deep biological insights and identifying potential therapeutic targets from complex data. This approach not only fast-tracks the development of novel, effective drugs with fewer side effects but also streamlines candidate selection for clinical trials, significantly reducing time and costs. Our use of causal AI ensures more efficient and targeted drug development, promising improved treatments for patients.