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 or transcriptomics is costly, invasive, or impossible, leading to dropped subjects and missed biomarker opportunities.
Solution
Strand offers an AI platform that infers missing biological modalities—like proteomic, transcriptomic, or other biomarker profiles—from existing data sources such as H&E pathology slides and genomic information. By generating synthetic measurements, the platform enables scientists to retain incomplete subjects, avoid expensive additional assays, and discover predictive signatures that would otherwise remain hidden. The imputed data can be integrated into downstream analyses, improving patient stratification for oncology and rare‑disease studies without requiring new sample collection. Strand’s models are trained on large multimodal datasets, ensuring that predictions reflect biologically plausible relationships between modalities.
Target Audience
Primary customers are pharmaceutical and biotech research teams conducting oncology or rare‑disease studies that need comprehensive biomarker data for patient selection and cohort analysis.
Features
- AI-driven imputation of proteomics, transcriptomics, and other biomarkers from histology images and genotype data
- Compatibility with existing patient datasets, allowing seamless integration into current analysis pipelines
- Scalable cloud infrastructure that processes whole cohorts to generate complete multimodal profiles
- Validation framework that quantifies prediction confidence and highlights high‑certainty imputations
- Export of imputed data in standard formats for downstream statistical and machine‑learning workflows