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Artificial Fish Swarm Optimization Based Method to Identify Essential Proteins.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2018-08-15 , DOI: 10.1109/tcbb.2018.2865567
Xiujuan Lei , Xiaoqin Yang , Fangxiang Wu

It is well known that essential proteins play an extremely important role in controlling cellular activities in living organisms. Identifying essential proteins from protein protein interaction (PPI) networks is conducive to the understanding of cellular functions and molecular mechanisms. Hitherto, many essential proteins detection methods have been proposed. Nevertheless, those existing identification methods are not satisfactory because of low efficiency and low sensitivity to noisy data. This paper presents a novel computational approach based on artificial fish swarm optimization for essential proteins prediction in PPI networks (called AFSO_EP). In AFSO_EP, firstly, a part of known essential proteins are randomly chosen as artificial fishes of priori knowledge. Then, detecting essential proteins by imitating four principal biological behaviors of artificial fishes when searching for food or companions, including foraging behavior, following behavior, swarming behavior and random behavior, in which process, the network topology, gene expression, gene ontology (GO) annotation and subcellular localization information are utilized. To evaluate the performance of AFSO_EP, we conduct experiments on two species (Saccharomyces cerevisiae and Drosophila melanogaster), the experimental results show that our method AFSO_EP achieves a better performance for identifying essential proteins in comparison with several other well-known identification methods, which confirms the effectiveness of AFSO_EP.

中文翻译:

基于人工鱼群优化的识别必需蛋白质的方法。

众所周知,必需蛋白质在控制活生物体的细胞活动中起着极其重要的作用。从蛋白质相互作用(PPI)网络中识别必需蛋白质有助于理解细胞功能和分子机制。迄今为止,已经提出了许多必需的蛋白质检测方法。然而,由于低效率和对噪声数据的低敏感性,那些现有的识别方法并不令人满意。本文提出了一种基于人工鱼群优化的新颖计算方法,用于预测PPI网络(称为AFSO_EP)中的必需蛋白质。在AFSO_EP中,首先,随机选择一部分已知必需蛋白作为先验知识的人工鱼。然后,通过在寻找食物或伴侣时模仿人造鱼的四种主要生物学行为来检测必需蛋白,包括觅食行为,跟随行为,群体行为和随机行为,在此过程中,网络拓扑结构,基因表达,基因本体(GO)注释和利用亚细胞定位信息。为了评估AFSO_EP的性能,我们对两种酵母(酿酒酵母和果蝇Drosophila melanogaster)进行了实验,实验结果表明,与其他几种著名的鉴定方法相比,我们的AFSO_EP方法在鉴定必需蛋白质方面表现出更好的性能。 AFSO_EP的有效性。群体行为和随机行为,其中利用了网络拓扑,基因表达,基因本体(GO)注释和亚细胞定位信息。为了评估AFSO_EP的性能,我们对两种酵母(酿酒酵母和果蝇Drosophila melanogaster)进行了实验,实验结果表明,与其他几种著名的鉴定方法相比,我们的AFSO_EP方法在鉴定必需蛋白质方面表现出更好的性能。 AFSO_EP的有效性。群体行为和随机行为,其中利用了网络拓扑,基因表达,基因本体(GO)注释和亚细胞定位信息。为了评估AFSO_EP的性能,我们对两种酵母(酿酒酵母和果蝇Drosophila melanogaster)进行了实验,实验结果表明,与其他几种著名的鉴定方法相比,我们的AFSO_EP方法在鉴定必需蛋白质方面表现出更好的性能。 AFSO_EP的有效性。
更新日期:2020-04-22
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