胰腺癌转化研究中通过系统建模在蛋白质组学数据库中进行知识发现。
Knowledge Discovery in Databases of Proteomics by Systems Modeling in Translational Research on Pancreatic Cancer.
作者信息
Resell Mathilde, Graarud Elisabeth Pimpisa, Rabben Hanne-Line, Sharma Animesh, Hagen Lars, Hoang Linh, Skogaker Nan T, Aarvik Anne, Svensson Magnus K, Amrutkar Manoj, Verbeke Caroline S, Batra Surinder K, Qvigstad Gunnar, Wang Timothy C, Rustgi Anil, Chen Duan, Zhao Chun-Mei
机构信息
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
PROMEC-Proteomics and Modomics Experimental Core Facility at NTNU and the Central Norway Regional Health Authority, 7030 Trondheim, Norway.
出版信息
Proteomes. 2025 May 29;13(2):20. doi: 10.3390/proteomes13020020.
BACKGROUND
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.
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 (cells, spheroids and organoids) and (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.
背景
数据库知识发现（KDD）可通过弥合研究与临床应用之间的差距，为转化研究（也称为转化医学）做出贡献。在此，我们提出一种用于KDD的“系统建模”工作流程。
方法
该框架包括组成模型（各种研究模型）、处理模型（蛋白质组学）和分析模型（生物信息学、人工智能/机器学习和模式评估）的数据收集、知识呈现以及用于假设生成和验证的反馈回路。我们应用此工作流程来研究胰腺导管腺癌（PDAC）。
结果
我们确定了人类PDAC与各种研究模型（细胞、球体和类器官）以及（小鼠）之间的共同蛋白质。据此，我们对枢纽蛋白和相关信号通路、PDAC特异性蛋白和特征通路以及高拓扑蛋白上的潜在转化靶点进行了假设。
结论
这种系统建模工作流程可以成为KDD的一种有价值的方法，总体上有助于在转化靶点中发现知识，特别是在这种情况下对胰腺导管腺癌的研究。
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