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一种基于种子扩张的方法,通过整合蛋白质-蛋白质相互作用子网络和多种生物特征来识别必需蛋白质

He Zhao^1,2, Guixia Liu^3,4, Xintian Cao^1,2

¹College of Computer Science and Technology, Jilin University, Changchun, China.

BMC bioinformatics

|November 30, 2023

概括

识别必要的蛋白质对于生物学和病理学至关重要. 我们的新型SESN方法通过蛋白质-蛋白质相互作用 (PPI) 子网络和多种生物特征来改善基本蛋白质预测,优于现有的方法.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
生物信息学是一种生物信息学.

背景情况:

在生物学和病理学中,基本蛋白质的识别至关重要.
高通量蛋白-蛋白相互作用 (PPI) 数据通常含有错误阳性.
需要使用生物和拓特征的计算算法来识别必要的蛋白质.

研究的目的:

提出一种新的种子扩张方法,SESN,用于准确识别必需蛋白质.
为了利用蛋白质-蛋白质相互作用 (PPI) 子网络和多种生物特征来改善预测.
根据现有方法评估SESN的性能.

主要方法:

通过基因表达数据,SESN构建PPI子网络.
它采用基于拓特征的子网络内的同时种子扩张.
一个错误纠正机制使用多个生物特征和整个PPI网络.

主要成果:

SESN集成了基因表达,蛋白质复合体,GO注释和亚细胞局部化数据.
该方法分析并选择最有效的生物特征进行预测.
输出是一组精细的预测必需蛋白质.

结论:

SESN 方法的所有组成部分都对其有效性有所贡献.
在多个数据集和物种上的比较实验证明了SESN的卓越性能.
SESN提供了一种强大的方法来识别必需蛋白质.

关键词:

生物数据生物数据重要的蛋白质是必不可少的.PPI子网络 PPI子网络种子扩张种子扩张

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