Knowledge Discovery in Databases of Proteomics by Systems Modeling in Translational Research on Pancreatic Cancer
Background: Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between <i>in vitro</i> and <i>in vivo</i> studies, and clinical applications. Here, we propose a ‘systems modeling’ workflow...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Proteomes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7382/13/2/20 |
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| Summary: | Background: Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between <i>in vitro</i> and <i>in vivo</i> studies, and clinical applications. Here, we propose a ‘systems modeling’ workflow for KDD. Methods: This framework includes the data collection of a composition model (various research models), processing model (proteomics) and analytical model (bioinformatics, artificial intelligence/machine leaning and pattern evaluation), knowledge presentation, and feedback loops for hypothesis generation and validation. We applied this workflow to study pancreatic ductal adenocarcinoma (PDAC). Results: We identified the common proteins between human PDAC and various research models <i>in vitro</i> (cells, spheroids and organoids) and <i>in vivo</i> (mouse mice). Accordingly, we hypothesized potential translational targets on hub proteins and the related signaling pathways, PDAC-specific proteins and signature pathways, and high topological proteins. Conclusions: This systems modeling workflow can be a valuable method for KDD, facilitating knowledge discovery in translational targets in general, and in particular to PADA in this case. |
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| ISSN: | 2227-7382 |