Detection of protein complexes from multiple protein interaction networks using graph embedding

• Published in: Artificial intelligence in medicine Vol. 96; pp. 107 - 115
• Main Authors: Liu, Xiaoxia, Yang, Zhihao, Sang, Shengtian, Lin, Hongfei, Wang, Jian, Xu, Bo
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-05-2019
• Subjects: Algorithms; Humans; Network embedding; Protein complex identification; Protein Interaction Mapping - methods; Proteins - chemistry; Protein–protein interaction networks; Protein–protein interaction networks; Protein complex identification; Network embedding
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2019.04.001

[Display omitted]
•Integrate multiple PPI datasets from different species into a single network.•Use a graph embedding method to encode protein nodes into continuous vector spaces.•Quantify the relations based on their corresponding node embedding similarities.•Utilize a seed-and-extend strategy to identify protein complexes.
Cellular processes are typically carried out by protein complexes rather than individual proteins. Identifying protein complexes is one of the keys to understanding principles of cellular organization and function. Also, protein complexes are a group of interacting genes underlying similar diseases, which points out the therapeutic importance of protein complexes. With the development of life science and computing science, an increasing amount of protein–protein interaction (PPI) data becomes available, which makes it possible to predict protein complexes from PPI networks. However, most PPI data produced by high-throughput experiments often has many false positive interactions and false negative edge loss, which makes it difficult to predict complexes accurately. In this paper, we present a new method, named as MEMO (Multiple network Embedding for coMplex detectiOn), to detect protein complexes. MEMO integrates multiple PPI datasets from different species into a single PPI network by using functional orthology information across multiple species and then uses a graph embedding technology to embed protein nodes of the network into continuous vector spaces, so as to quantify the relationships between nodes and better guild the protein complex detection process. Finally, it utilizes a seed-and-extend strategy to identify protein complexes from multiple PPI networks based on the similarities of their corresponding protein representations. As part of our approach, we also define a new quality measure which combines the cluster cohesiveness and cluster density to measure the likelihood of a detected protein complex being a real protein complex. Extensive experimental results demonstrate the proposed method outperforms state-of-the-art complex detection techniques.