多方面的进化重点是最大限度地利用领域知识,以便在基因监管网络的共识推断中取得共识
Adrián Segura-Ortiz1, Karen Giménez-Orenga2, José García-Nieto3
1Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain.
Computers in biology and medicine
|July 2, 2025
概括
通过将多种方法与生物目标相结合,BIO-INSIGHT优化了基因调节网络 (GRN) 的推断. 这种以生物学为导向的方法显著提高了比数学方法的准确性,有助于发现纤维肌痛等疾病的生物标志物.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调节网络 (GRN) 的推断对于从表达数据中理解基因相互作用至关重要.
- 传统方法面临着结果差异和数据集偏差的挑战.
- 需要更准确和生物相关的GRN推断工具.
研究的目的:
- 引入BIO-INSIGHT,一种用于推断基因调节网络的新算法.
- 通过使用生物目标优化多种方法之间的共识来增强GRN推断.
- 提高推断的GRNs的准确性和生物可行性.
主要方法:
- 开发了BIO-INSIGHT,一个并行异步的多目标进化算法.
- 通过生物目标指导的多种GRN推断方法实现优化共识.
- 在106个GRNs的基因基因表达基因基因基因基因基因基因基因表达基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因
主要成果:
- 与现有方法相比,BIO-INSIGHT在AUROC和AUPR中显示出了统计学上显著的改进.
- 生物指导优化被证明优于纯粹的数学方法.
- 纤维肌痛和肌痛性脑筋炎的推断网络揭示了特定疾病的调节相互作用.
结论:
- 生物洞察提供了一个强大的和创新的工具,用于生成准确的,生物可行的GRNs.
- 该算法显示在识别生物标志物和治疗点方面具有临床实用性的潜力.
- 该软件作为PyPI上的Python库和GitHub上的源代码公开提供.
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