The biomedical data landscape is fragmented, with isolated data sources and knowledge bases that employ different syntaxes, schemas, entity notations, and metadata standards. Formats vary based on the type of data generated (imaging, sequencing data, documents, EHR, Flow Cytometry, etc.), and data is often inconsistently labeled & devoid of context.
Therefore, despite the availability of data, scientists still face significant logistical and technical challenges when hosting, integrating, & analyzing heterogeneous data collected from a variety of sources. Additionally, these data may require exhaustive computational resources and a unified querying interface for processing and in-depth data exploration.
Elucidata’s Polly is a Data-Centric platform architected to treat data as the continual focus. It manages the entire lifecycle of a biomedical dataset - from ingestion & harmonization, processing & preparing the data for AI/ML and pushing to downstream applications. Polly allows scientists to aggregate data generated from public repositories and proprietary experiments on a centralized Enterprise OmixAtlas, and mine for novel associations from these diverse sources to enhance the quality of their hypotheses. In addition, scientists can leverage the following capabilities of an OmixAtlas on Polly, to enable data-driven research:
-Model-assisted curation for harmonizing datasets with ontology-backed metadata and ensuring they conform to an in-house data model.
-An SQL-based programmatic interface that allows complex querying
-A scalable computational platform to analyze and visualize data.
-API-based access to stream Ml-Ready data from an OmixAtlas to data warehouses, software, BI tools, etc.