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

Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between and studies and clinical applications. Here, we propose a 'systems modeling' workflow for KDD. This framework includes data collection of composition model (various research models) and processing model (proteomics) and analytical model (bioinformatics, artificial intelligence/machine leaning and pattern evaluation), knowledge presentation, and feedback loops for hypothesis generation and validation.

Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models.