Create and maintain biomedical ontologies for consistent EHR data integration, with continuous updates, robust quality checks, and a cohort builder for in-depth analysis.
Analysis ready EHR data with LLM-validation layer.
Comprehensive and flexible framework for storing, retrieving, and analyzing data.
Custom common data model based on OMOP standards.
Expert metadata curation with in-depth quality checks.
Create and manage ontologies and vocabularies that define relationships within biomedical knowledge, facilitate consistent data representation, and accelerate research.
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Leverage the latest relationships and hierarchies with curated ontologies and custom vocabularies from recently published knowledge.
Easily record and manage changes with version control, ensuring reproducibility and traceability.
Set of integrated checks and processes designed to automatically evaluate and ensure accuracy, completeness, and data consistency.
Identify and address errors, inconsistencies, and missing values at the source and post-curation through rigorous checks at ingestion, curation, and mapping stages.
Generate tailored reports to explore data quality and analyze correlations between data elements.
Define and build specific groups or cohorts based on criteria like demographics, conditions, clinical outcomes, and biomarker status from assays and omics data.
Enable users to identify and analyze cohorts of interest, isolating and studying relevant data subsets. Interact with data through notebooks or a GUI, save and share cohorts for reproducibility.
Ensure greater control over data security, compliance, and customization, allowing users to manage and tailor their infrastructure according to their specific needs.
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Request extra metadata fields, use custom ontologies, or annotate cell types with your preferred marker database.
Consistently process, annotate, and QC single-cell data using scientifically validated Polly pipelines to ensure data interoperability.
Seamlessly integrate Polly into your existing infrastructure! Automate ingestion of in-house data from your data storage (ELN, S3 bucket, CROs, and more) into a central Atlas on Polly.
Focus on discovery, not data wrangling! Polly automatically cleans, harmonizes, and structures your in-house single-cell datasets, ensuring they adhere to your custom schema.
Integrate multi-modal datasets into one central Atlas to unveil hidden patterns, and expedite research breakthroughs.
Effortlessly manage and analyze TBs of both in-house and public single-cell data on Polly's secure cloud.
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All single-cell datasets delivered by Polly undergo ~50 QA checks to ensure quality and provenance.
Assess the intrinsic quality of the data (genes, cells, measurements) with comprehensive QA reports detailing the processing methodology.
Avail unrestricted data connectivity and consumption between Polly and your preferred analysis environments. Use APIs to stream harmonized data on Polly to external tools and applications.
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Build predictive models using EHR data to identify high-risk patients and detect early biomarker signals for conditions like diabetes and heart disease before symptoms arise.
Leverage EHR data to craft personalized treatment plans based on patient histories, including past treatment responses, environmental exposures, and comorbid conditions.
Use EHR data to identify and recruit eligible patients based on medical history and biomarker status, ensuring the right participants for clinical trials. Gather real-world evidence on drug efficacy and safety to complement controlled trial findings.
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Use ML-ready datasets to accelerate insight generation from detailed, diverse, and dynamic EHR data. Access and analyze this data to make informed decisions, develop effective treatments, and improve patient care.
Data processing and scientific reporting of metabolomics data are crucial but also tedious. Polly’s range of applications helped our team in accelerating these processes significantly and effectively.
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