Wang Wenkang, Meng Xiangmao, Xiang Ju, Dino Bedru Hayat, Li Min
IEEE/ACM Trans Comput Biol Bioinform. 2024 Nov-Dec;21(6):2000-2010. doi: 10.1109/TCBB.2024.3429546. Epub 2024 Dec 10.
Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only focus on local information of proteins in the PPI network, which are easily affected by the noise in the PPI network. Meanwhile, it's still challenging to detect protein complexes, especially for overlapping cases. To address these issues, we propose a new method, named Dopcc, to detect overlapping protein complexes by constructing a multi-metrics network according to different strategies. First, we adopt the Jaccard coefficient to measure the neighbor similarity between proteins and denoise the PPI network. Then, we propose a new strategy, integrating hierarchical compressing with network embedding, to capture the high-order structural similarity between proteins. Further, a new co-core attachment strategy is proposed to detect overlapping protein complexes from multi-metrics. The experimental results show that our proposed method, Dopcc, outperforms the other eight state-of-the-art methods in terms of F-measure, MMR, and Composite Score on two yeast datasets.
蛋白质复合物的识别是系统生物学领域的一个重要问题，对于理解细胞组织和推断蛋白质功能至关重要。最近，人们提出了许多计算方法来从蛋白质 - 蛋白质相互作用（PPI）网络中检测蛋白质复合物。然而，这些方法大多只关注PPI网络中蛋白质的局部信息，容易受到PPI网络中噪声的影响。同时，检测蛋白质复合物仍然具有挑战性，特别是对于重叠情况。为了解决这些问题，我们提出了一种名为Dopcc的新方法，通过根据不同策略构建多指标网络来检测重叠蛋白质复合物。首先，我们采用杰卡德系数来衡量蛋白质之间的邻居相似度并对PPI网络进行去噪。然后，我们提出了一种新策略，将层次压缩与网络嵌入相结合，以捕捉蛋白质之间的高阶结构相似性。此外，还提出了一种新的共核心附着策略，从多指标中检测重叠蛋白质复合物。实验结果表明，我们提出的方法Dopcc在两个酵母数据集上的F值、MMR和综合得分方面优于其他八种最先进的方法。
