To develop an integrated framework for biopharmaceutical process optimization using text mining and knowledge graph modeling.
Key Findings:
The knowledge graph enables visualization of interconnected variables across multiple studies.
Users can query the graph to explore associations and trace relationships back to source publications.
The framework supports literature review, hypothesis generation, and experimental planning.
Interpretation:
The integrated framework enhances the ability to synthesize findings from diverse studies in biopharmaceutical production, potentially improving process optimization.
Limitations:
System performance relies on the consistency and completeness of published data.
Variability in terminology across studies may hinder entity recognition and relationship mapping.
The framework does not replace the need for experimental validation.
Conclusion:
The study highlights the potential of combining automated text extraction with graph-based modeling in biopharmaceutical process development, though further evaluation is needed for practical application.
