Strand provides an AI platform that predicts missing biological modalities—such as proteomics, transcriptomics, or other biomarkers—from existing patient data like H&E slides and genotypes.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers often lack complete multimodal biological data for patient cohorts because acquiring assays such as proteomics, transcriptomics, or spatial protein maps is costly, invasive, or impossible for all samples. Missing biomarkers can lead to incomplete analyses, reduced statistical power, and suboptimal patient selection in oncology and rare disease studies.
Solution
Strand AI offers a multimodal artificial‑intelligence platform that predicts unmeasured biological modalities from existing data sources, such as H&E pathology slides and genomic information. By training foundation models on large, multimodal patient datasets, the system can generate spatial protein maps, gene‑expression profiles, and other biomarker readouts that were never experimentally assayed. This imputation enables researchers to rescue incomplete cohorts, avoid expensive additional assays, and discover predictive signatures across the full dataset. The platform delivers these predictions as ready‑to‑use data layers that can be integrated into downstream statistical or machine‑learning pipelines, improving study power without extra sample collection.
Target Audience
Primary customers are biomedical researchers and data scientists conducting oncology or rare‑disease studies who need comprehensive multimodal datasets but lack complete assay coverage.
Features
- AI models that infer proteomic and transcriptomic profiles from routine H&E slides
- Genotype‑to‑gene‑expression imputation using large‑scale multimodal training data
- Generation of 180+ spatial protein maps per slide, enabling virtual spatial proteomics
- Compatibility with existing bioinformatics workflows for seamless integration of imputed data
- Scalable cloud‑based inference pipeline that processes whole‑slide images and genomic files