IEEE/ACM Trans Comput Biol Bioinform. 2020 Mar-Apr;17(2):495-505. doi: 10.1109/TCBB.2018.2865567. Epub 2018 Aug 15.
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, first, 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.
众所周知，必需蛋白质在控制生物体内的细胞活动方面起着极其重要的作用。从蛋白质-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蛋白质检测方法。然而，由于效率低和对噪声数据的敏感性低，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的计算方法，用于预测 PPI 网络中的必需蛋白质（称为 AFSO_EP）。在 AFSO_EP 中，首先，随机选择一部分已知的必需蛋白质作为先验知识的人工鱼。然后，通过模仿人工鱼在寻找食物或同伴时的四种主要生物行为来检测必需蛋白质，包括觅食行为、跟随行为、聚集行为和随机行为，在此过程中，利用网络拓扑、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，我们在两种物种（酿酒酵母和黑腹果蝇）上进行了实验，实验结果表明，与其他几种著名的识别方法相比，我们的方法 AFSO_EP 在识别必需蛋白质方面具有更好的性能，这证实了 AFSO_EP 的有效性。
