Scholarlink

Using models to interpret data is a fundamental aspect of data analysis, enabling us to understand patterns, make predictions, and derive insights from raw information. In particular, using models transform raw or otherwise mute data into valuable knowledge. In this work we show how an intuition concerning Random Boolean Networks (RBNs) - a well-known model in the field of complex systems - allows the introduction of a new type of observable, useful for the analysis of real biological data. We then apply this idea to single-cell data on humans and mice.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using Pseudo-attractors of Genetic Regulatory Networks to Analyze Biological Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Gianluca D’Addese,
  • Carlo Crovetti,
  • Roberto Serra,
  • Marco Villani

摘要

Using models to interpret data is a fundamental aspect of data analysis, enabling us to understand patterns, make predictions, and derive insights from raw information. In particular, using models transform raw or otherwise mute data into valuable knowledge. In this work we show how an intuition concerning Random Boolean Networks (RBNs) - a well-known model in the field of complex systems - allows the introduction of a new type of observable, useful for the analysis of real biological data. We then apply this idea to single-cell data on humans and mice.