Knowledge Discovery in Databases of Proteomics by Systems Modeling in Translational Research on Pancreatic Cancer

Area of Science:

• Biomedical Informatics
• Translational Medicine
• Systems Biology

Background:

• Knowledge discovery in databases (KDD) is crucial for translational research, bridging basic science and clinical applications.
• A systems modeling workflow is proposed to enhance KDD for translational medicine.

Purpose of the Study:

• To develop and apply a systems modeling workflow for knowledge discovery.
• To identify potential translational targets for pancreatic ductal adenocarcinoma (PDAC).

Main Methods:

• A framework integrating data collection (composition, processing, analytical models), knowledge presentation, and feedback loops.
• Utilized proteomics, bioinformatics, artificial intelligence/machine learning, and pattern evaluation.
• Applied the workflow to study human PDAC and various experimental models (in vitro and in vivo).

Main Results:

• Identified common proteins between human PDAC and diverse research models.
• Generated hypotheses for translational targets, including hub proteins, PDAC-specific proteins, and key signaling pathways.
• Highlighted potential therapeutic strategies based on protein network topology.

Conclusions:

• The proposed systems modeling workflow is effective for KDD in translational medicine.
• Facilitates the discovery of translational targets, with demonstrated utility for PDAC research.