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识别癌症驱动路径基于嘴部养鱼算法

Wei Zhang^1,2, Xiaowen Xiang¹, Bihai Zhao^1,2

¹College of Computer Science and Engineering, Changsha University, Changsha 410022, China.

Entropy (Basel, Switzerland)

|June 28, 2023

概括

这项研究引入了一种新的口鱼 (MBF) 算法,用于在通路水平上识别癌症驱动基因. 通过考虑突变异质性,MBF算法提高了准确性,超过了现有的方法.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

识别癌症驱动基因对于理解癌症和开发个性化治疗至关重要.
现有的方法在识别驱动路径时经常忽视突变异质性.
主要组件分析 (PCA) 可以帮助减少复杂性,并考虑共变量数据.

研究的目的:

开发一种用于识别癌症驱动基因在途径水平的新方法.
通过结合突变异质性来解决现有方法的局限性.
提高已识别的驱动基因的准确性和生物相关性.

主要方法:

利用了口鱼 (MBF) 算法,这是一个智能优化技术.
构建了一个最大重量子矩阵模型,将PCA纳入共变量数据.
为覆盖和独家性分配差异权重,以减轻突变异质.
在肺腺癌和质母细胞瘤多形数据集上测试了该方法.

主要成果:

在两个数据集中,MBF方法在10大小的驾驶员路径中实现了80%的识别准确度.
与MDPFinder,Dendrix和Mutex方法相比,MBF表现出更高的性能.
路径丰富分析证实了MBF识别的驱动基因的生物学意义.

结论:

该MBF算法有效地识别癌症驱动基因在途径水平,克服了以前方法的局限性.
该方法能够考虑突变异质性的能力提高了准确性.
已识别的驱动基因在癌症信号通路中起着重要作用,验证了MBF方法.

关键词:

生物效应生物效应生物效应癌症的进展癌症的进展司机基因覆盖范围司机基因覆盖范围司机路径的路径

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