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

Publication date: Available online 9 April 2019Source: Artificial Intelligence in MedicineAuthor(s): Xiaoxia Liu, Zhihao Yang, Shengtian Sang, Hongfei Lin, Jian Wang, Bo XuAbstractCellular 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 ...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research