基于并行离散人工蜂群算法从单细胞数据中推断因果蛋白质信号网络

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jinduo Liu, Jihao Zhai, Junzhong Ji
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引用次数: 0

摘要

从人类免疫系统细胞数据中推断因果蛋白质信号网络是揭示潜在组织信号生物学和病变细胞功能障碍的一种有前途的方法,在生物信息学领域引起了广泛关注。最近,贝叶斯网络(BN)技术在从多参数单细胞数据推断因果蛋白质信号网络方面大受欢迎。然而,目前的贝叶斯网络方法可能会表现出较高的计算复杂性,并且会忽略来自不同单细胞的蛋白质信号分子之间的相互作用。本文提出了一种基于并行离散人工蜂群(PDABC)的新型蛋白质信号网络学习方法,命名为 PDABC。具体来说,PDABC 是一种基于分数的 BN 方法,它利用并行人工蜂群来搜索具有最高离散 K2 指标的全局最优因果蛋白质信号网络。在几个模拟数据集以及之前发表的多参数荧光激活细胞分拣机数据集上的实验结果表明,PDABC在性能和计算效率方面都超越了现有的先进方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm

Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm

Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi-parameter fluorescence-activated cell sorter dataset, indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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