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Updated: Jan 7, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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PIPI-C:一种组合优化框架,用于在质谱数据中识别翻译后修改热点

Shengzhi Lai1, Shuaijian Dai2, Peize Zhao3

  • 1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

Molecular & cellular proteomics : MCP
|December 24, 2025
PubMed
概括

识别具有多个翻译后修饰 (PTM) 的对于理解癌症至关重要. 一个新的工具,PIPI-C,使用组合模型有效地检测复杂的PTM交叉声,揭示疾病特异性模式.

关键词:
计算型蛋白质组学是一种计算型蛋白质组学.混合整数线性编程 (MILP) 是一种线性编程.在 PTM 交叉的交叉中,酸标识 酸标识翻译后修改 (PTM) 是一种方法.

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