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

IF 4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Proteomes Pub Date : 2025-05-29 DOI:10.3390/proteomes13020020

Mathilde Resell, Elisabeth Pimpisa Graarud, Hanne-Line Rabben, Animesh Sharma, Lars Hagen, Linh Hoang, Nan T Skogaker, Anne Aarvik, Magnus K Svensson, Manoj Amrutkar, Caroline S Verbeke, Surinder K Batra, Gunnar Qvigstad, Timothy C Wang, Anil Rustgi, Duan Chen, Chun-Mei Zhao

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引用次数: 0

Abstract

Background: Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between in vitro and in vivo 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 in vitro (cells, spheroids and organoids) and in vivo (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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胰腺癌转化研究中基于系统建模的蛋白质组学数据库知识发现。

背景：数据库中的知识发现（KDD）可以通过弥合体外和体内研究以及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。在这里，我们为KDD提出了一个“系统建模”工作流。方法：该框架包括组合模型（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。我们将此工作流程应用于胰腺导管腺癌（PDAC）的研究。结果：我们在体外（细胞、球状体和类器官）和体内（小鼠）多种研究模型中鉴定出了人类PDAC与它们之间的共同蛋白。因此，我们假设了枢纽蛋白和相关信号通路、pdac特异性蛋白和信号通路以及高拓扑蛋白的潜在翻译靶点。结论：该系统建模工作流对于KDD来说是一种有价值的方法，一般来说可以促进转化目标中的知识发现，在这种情况下特别是对于PADA。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proteomes Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry

CiteScore

6.50

自引率

3.00%

发文量

审稿时长

11 weeks

期刊介绍： Proteomes (ISSN 2227-7382) is an open access, peer reviewed journal on all aspects of proteome science. Proteomes covers the multi-disciplinary topics of structural and functional biology, protein chemistry, cell biology, methodology used for protein analysis, including mass spectrometry, protein arrays, bioinformatics, HTS assays, etc. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of papers. Scope: -whole proteome analysis of any organism -disease/pharmaceutical studies -comparative proteomics -protein-ligand/protein interactions -structure/functional proteomics -gene expression -methodology -bioinformatics -applications of proteomics