Expand your pipeline of validated targets using harmonized biomedical data on Polly.
Poor target selection is the primary cause of clinical trial failure. To identify viable targets with a high threshold of certainty, your data pool needs to be scientifically robust, sufficiently large, and multi-modality. Therefore, having access to data that is integrated, harmonized, uniformly processed, and clean is critical.
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Develop a customized Atlas with ML-ready data for bias-free target predictions.
Create a disease-specific Atlas comprising meticulously curated data enriched with critical metadata and engineered for seamless integration into target prediction models.
Enhance prediction accuracy and mitigate bias through model training with multi-modal, harmonized datasets.
Instill confidence in your predictions by combining consistently processed samples for robust results.
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Explore healthy and patient cohorts on Polly for a comprehensive molecular profiling of the disease being studied.
Conduct gene expression analysis to uncover differentially expressed signatures specific to the disease condition.
Identify potential candidate genes by assessing their druggability scores and cross-referencing with publicly available evidence.
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Cross-reference your results with published evidence using curated public data delivered on Polly.
Validate target reliability by meta-analyzing relevant studies on Polly.
Evaluate targets for sensitivity, specificity, and clinical utility with rigorous statistical analysis.
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Achieve 4X faster target identification with Polly’s high-specificity prediction using multi-omics data.
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Speed up target validation with Polly's curated public evidence, reducing clinical trial risks.
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Cut your dataset search time
by 75% with Polly's advanced search capabilities.
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