Data Managers and Scientists use Xbiom Smart Transformation to curate and transform clinical, nonclinical and biomarker data from data-lakes to a target model
Recommendation engine uses the ontologies and vocabularies referenced in the target data model definition to map and harmonize the transformed data. The smart transformers (AI) continually improve, learn and adaptively evolve as data managers intervene, assert or correct errors in transformation or make decisions on metadata, content and terminology recommendations.
Xbiom Smart Transformation is a global Data ingestion solution for Data Managers to rapidly handle disparate data from multiple sources, harmonize and normalize into a single Development and Research, FAIR repository. This process is supported and controlled by Xbiom Metadata Governance to ensure consistency of the sponsor proprietary ontologies.
Using a human-supervised machine learning, adaptable and self-healing curation engine, Data Managers can rapidly bring more relevant data and files into the repository for Data Analysts and Regulatory teams.