ECDEP:根据进化社区的发现和亚细胞局部化,识别基本蛋白质
Chen Ye1,2, Qi Wu1,2, Shuxia Chen1,2
1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China.
BMC genomics
|January 26, 2024
概括
我们开发了ECDEP,这是一种使用进化社区发现识别必需蛋白质的新方法. 这种方法有效地整合了动态基因表达和蛋白质-蛋白质相互作用网络,改善了跨物种的预测.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 基本蛋白质对于细胞功能和了解疾病至关重要.
- 目前的深度学习方法不充分利用基因表达数据和动态网络来预测必要的蛋白质.
- 有限的跨物种评估阻碍了现有的预测模型的通用性.
研究的目的:
- 引入ECDEP,一种基于进化社区发现的模型,用于增强基本蛋白质识别.
- 利用时间基因表达数据和动态蛋白质-蛋白质相互作用网络来提高预测准确性.
- 解决当前方法在探索动态生物网络和跨物种适用性方面的局限性.
主要方法:
- ECDEP将时间基因表达数据与蛋白质-蛋白质相互作用 (PPI) 网络集成,使用3西格玛规则创建动态网络.
- 边缘出生/死亡信息为进化社区发现算法提供燃料,以识别动态网络中的重叠社区.
- 支持矢量机递归特征消除 (SVM-RFE) 提取信息社区,结合亚细胞局部化进行分类.
主要成果:
- 在四种物种 (S. cerevisiae,H. sapiens,M. musculus,C. elegans) 中,ECDEP被评价为十种中心性,四种浅层机器学习和两种深度学习方法.
- 该模型在Homo sapiens数据集上实现了0.86的精度回忆曲线 (AP) 下的面积.
- 社区特征对分类做出了重大贡献,在Saccharomyces cerevisiae数据集上达到0.54的比例.
结论:
- 拟议的ECDEP方法有效地整合了网络动态,在各种数据集中展示了卓越的性能.
- 进化社区的发现提高了基因表达数据对分类任务的实用性.
- ECDEP为基本蛋白质预测提供了一个强大的框架,解决了以前方法的局限性.
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