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Updated: May 13, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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通过机器学习和结构性表征来预测蛋白质网络扰动的综合性方法

Bethany D Bengs1, Jules Nde2, Sreejata Dutta1

  • 1Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas, USA.

Journal of proteomics
|April 14, 2025
PubMed
概括

机器学习准确地预测了基因删除的INO80复杂网络变化,揭示了端粒维护和衰老中的关键组件和作用. 这种方法有助于理解染色体生物学和疾病变异.

关键词:
染色体重塑 染色体重塑 的方法机器学习是机器学习.扰乱网络中的扰乱.统计 统计 统计 统计

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