The wealth of standardized single-cell data provides a powerful foundation for drug research. Its scale and resolution enable AI/ML models to uncover complex biological patterns, while detailed cellular profiles support the identification of novel drug targets and biomarkers. By capturing heterogeneity across tissues and treatments, single-cell datasets offer critical insights for patient stratification and disease mechanisms studies.

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Yet public single-cell omics data are often fragmented across multiple repositories and stored in different raw formats, units and metadata standards. These lead to major challenges:

 

• Data scouting and collection: Single-cell data are scattered across multiple public and private repositories, making it difficult to identify and compile all datasets relevant to specific therapeutic areas, diseases, or conditions.

• Data cleaning and standardization: Datasets vary widely in quality, formats, and measurement units. Processing them at scale demands significant time and resources for quality control and standardization.

• Metadata harmonization: Authors often use inconsistent terminology when labeling data. This complicates dataset integration and cross-comparison, requiring substantial manual effort to review studies and align terms with standardized vocabularies.

• Data engineering: Even after standardization and harmonization, transforming datasets into a consistent data model for integration with in-house warehouses and systems requires considerable time and expertise.

• Data accessibility: Accessing and reanalyzing these datasets often requires programming skills, creating barriers for many team members from exploring the data.