Strand AI

Founded by Yue Dai

Curated multimodal datasets for biology AI

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You can use Strand AI to predict missing biological data like gene expression, proteomics, and spatial transcriptomics from routinely collected patient samples. It helps life sciences teams running clinical trials and biomarker discovery by filling gaps in multimodal patient datasets. Instead of collecting expensive new samples, you can predict the missing biological modalities from H&E slides and genotypes you already have. This allows you to rescue incomplete patient cohorts, find biomarkers you never measured, and unlock insights from rare disease studies with sparse data coverage.

What Strand AI does

Import existing patient sample data and clinical recordsTrain AI models on available biological modalitiesPredict missing gene expression profiles from slidesGenerate proteomics data from genotype inputsIdentify biomarkers across complete patient cohortsValidate predictions against known biological markersExport completed datasets for downstream analysisIntegrate results with existing research workflowsPredicts gene expression from H&E slides and genotype dataGenerates spatial transcriptomics without expensive assaysFills missing proteomics data from existing patient samplesRecovers incomplete patient cohorts for clinical trialsWorks with rare disease populations and sparse datasetsIdentifies biomarkers without re-acquiring patient samplesIncludes POSTMAN spatial proteomics prediction modelProcesses routinely collected clinical samples

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General Genomics Inc.

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