Strand AI: Strand AI uses foundation models to predict missing
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Strand AI

Strand AI uses foundation models to predict missing patient data, helping pharma teams select better trial candidates and bring effective drugs to market faster

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Overview

Unlock the Full Potential of Your Clinical Trials with Predictive Multimodal Patient Data

Strand AI helps life sciences teams overcome the limitations of incomplete patient data, a major contributor to the 90% failure rate of clinical trials, by predicting missing biological modalities from existing data, enabling more accurate patient selection and biomarker discovery.

Details

Key Features

  • Rescue incomplete cohorts by predicting missing modalities, reducing the need for additional invasive and expensive assays
  • Generate high-quality multimodal patient data, including gene expression, proteomics, and spatial transcriptomics, from routinely collected samples
  • Unlock rare disease cohorts by filling in missing modalities, enabling more effective model training and biomarker discovery
  • Impute unmeasured biomarkers across entire cohorts, surfacing predictive signatures without the need for re-acquisition

Best For

  • Life sciences teams running clinical trials and seeking to improve patient selection and biomarker discovery
  • Researchers working with rare disease cohorts and requiring more comprehensive and accurate data
  • Biopharmaceutical companies aiming to reduce the cost and time associated with clinical trials and improve drug development outcomes
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Key Features

  • Rescue incomplete cohorts by predicting missing modalities, reducing the need for additional invasive and expensive assays

  • Generate high-quality multimodal patient data, including gene expression, proteomics, and spatial transcriptomics, from routinely collected samples

  • Unlock rare disease cohorts by filling in missing modalities, enabling more effective model training and biomarker discovery

  • Impute unmeasured biomarkers across entire cohorts, surfacing predictive signatures without the need for re-acquisition

  • Streamline clinical trial workflows by skipping the expensive and time-consuming process of acquiring every modality for every patient

Ideal For

  • Life sciences teams running clinical trials and seeking to improve patient selection and biomarker discovery

  • Researchers working with rare disease cohorts and requiring more comprehensive and accurate data

  • Biopharmaceutical companies aiming to reduce the cost and time associated with clinical trials and improve drug development outcomes

Ideal for

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