Learn how data on Polly is integrated, harmonized, uniformly processed and made ‘ML-ready’.
Polly's powerful harmonization engine processes measurements, links to harmonized metadata and transforms them into a Unified Data Model.
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Mapped with ontology-backed metadata at dataset, sample / cell, and feature levels. 99% accuracy and metadata completeness. All labels are human-readable, searchable, and relevant to the disease biology being studied.
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Built on a powerful, customizable curation engine that transforms various kinds of omics, assay and clinical data into a high-quality, ML-ready resource. Customize the pipeline in which your data is processed, metadata harmonized or data model applied.
Harmonizing molecular data requires unprecedented scale in terms of technology and the computational power required. Polly ingests and processes over 35 TBs of molecular data every month. Our purpose-built infrastructure ensures secure storage and real-time data processing, enabling swift analysis for faster target discoveries.
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Compare a harmonized dataset on Polly with un-annotated data from the source publication.
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Polly's harmonization engine has driven ML readiness for millions of datasets.
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ETL pipelines built for multi modality biological data.
Datasets processed and curated across projects.
Samples per day processed and harmonized.
Faster with LLM-powered harmonization.
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Data harmonization is the process of integrating and standardizing raw data from diverse sources into a unified data model, improving data quality and making it ML-ready.
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In medical terms, harmonization is the process of ensuring consistency in clinical trial results, patient records, and diagnostic data across healthcare systems. This helps in accurate analysis and supports better patient care and medical research.
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In biology, harmonization involves standardizing diverse biological datasets, like gene expression or genomic sequences, generated from various experimental techniques, instruments, and laboratories.
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Data harmonization standardizes data from multiple sources for consistency, while Master Data Management (MDM) creates a single authoritative source of truth for key data entities. Both are essential for effective data management, but harmonization specifically optimizes data for scientific research and AI applications.
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For biomedical R&D, data harmonization integrates and standardizes clinical, genomic, and proteomic data from various sources. This ensures data consistency for accurate analysis in biomarker discovery and clinical applications.
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