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.
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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.
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.
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.
Polly is an AI-ready data platform that accelerates target identification by integrating and harmonizing multi-omics data. It provides machine-learning-ready datasets, enabling precise biomarker discovery and validation for drug development.
Multi-modal data is crucial for effective target validation as it integrates diverse biological datasets such as genomics, transcriptomics, proteomics, and metabolomics to provide a comprehensive view of disease mechanisms. This approach enhances accuracy in identifying viable therapeutic targets, reducing clinical trial failures.
Polly standardizes biomedical data by applying rigorous curation and metadata enrichment. It delivers harmonized, high-quality datasets, ensuring accuracy, consistency, and seamless AI/ML integration for better target prediction.
Robust, harmonized data improves target selection, reducing clinical trial failures. Polly provides clean, well-structured datasets, leading to better predictions, lower false discovery rates, and higher clinical success.
Polly accelerates validated target discovery by enabling researchers to create disease-specific multi-omics atlases. These ML-ready datasets enhance prediction accuracy and streamline drug development.
Clean, integrated biomedical data improves prediction accuracy, minimizes biases, and enhances biomarker discovery. Polly’s harmonized datasets drive more confident decision-making, reducing false leads, and accelerating therapeutic advancements.
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