TetraScience

Integrate transcriptomics, proteomics, and cell-based assay data into the TetraScience platform.

Harmonize data using predefined data models and ontologies.

Apply statistical analysis and machine learning to identify correlations between target expression and phenotypic outcomes.

Visualize results using interactive dashboards to identify promising drug targets.

Connect bioreactors, analytical instruments, and process control systems to the TetraScience platform.

Stream real-time data into the platform and harmonize it.

Set up automated alerts for out-of-specification conditions.

Use machine learning to optimize process parameters based on historical data and real-time feedback.

Ingest data from HPLC, DSC, XRPD, and other analytical instruments into the TetraScience platform.

Harmonize data using predefined data models for formulation components and properties.

Apply statistical modeling to predict formulation stability and performance.

Generate reports summarizing key formulation characteristics and optimization recommendations.

Connect QC instruments and LIMS to the TetraScience platform.

Automate data transfer and validation from QC instruments.

Implement standardized QC testing procedures and workflows.

Generate automated QC reports and release certificates.

Integrate genomics, transcriptomics, proteomics, and metabolomics data into the TetraScience platform.

Harmonize data using common ontologies and data standards.

Apply machine learning algorithms to identify potential biomarkers.

Validate biomarkers in independent patient cohorts.