Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

Abstract

Over the past decades, massive amounts of protein-protein interaction (PPI) data have been accumulated due to the advancement of high-throughput technologies, and but data quality issues (noise or incompleteness) of PPI have been still affecting protein function prediction accuracy based on PPI networks. Although two main strategies of network reconstruction and edge enrichment have been reported on the effectiveness of boosting the prediction performance in numerous literature studies, there still lack comparative studies of the performance differences between network reconstruction and edge enrichment. Inspired by the question, this study first uses three protein similarity metrics (local, global and sequence) for network reconstruction and edge enrichment in PPI networks, and then evaluates the performance differences of network reconstruction, edge enrichment and the original networks on two real PPI datasets. The experimental results demonstrate that edge enrichment work better than both network reconstruction and original networks. Moreover, for the edge enrichment of PPI networks, the sequence similarity outperformes both local and global similarity. In summary, our study can help biologists select suitable pre-processing schemes and achieve better protein function prediction for PPI networks.

LIRcentral: a manually curated online database of experimentally validated functional LIR-motifs

“…Protein-protein interaction (PPI) networks are becoming increasingly popular, since they facilitate the enhanced description and understanding of cellular systems in physiological conditions and disease [37]. They can also aid in deciphering the biological function of proteins [38] and the prediction of protein complexes [39]. Additionally, PPI networks can provide useful insights on potential interacting partners, enabling experimental biologists to prioritize research targets.…”

“…They can also aid in deciphering the biological function of proteins [38] and the prediction of protein complexes [39]. Additionally, PPI networks can provide useful insights on potential interacting partners, enabling experimental biologists to prioritize research targets.…”

Identification of key genes in hepatitis B based on WGCNA and machine learning

“…4.B). The core genes of HBV disease were analyzed by protein-protein interaction (PPI) [20] through the STRING background network, and the importance of genes was assessed according to the Betweenness centrality(BC), and the size of the circle was directly proportional to the importance of PPI assessment (Fig. 4.A).…”

Exploration of potential targets and mechanisms of Naringenin in treating autism spectrum disorder via network pharmacology and molecular docking

“…The construction of PPI is crucial for understanding the indication to what extent 2 proteins interact with each other. [ 28 ] We imported the potential targets obtained above into the STRING [ 29 ] ( https://string-db.org ) platform. “Organism” was set to “Homo Sapiens,” free nodes were hidden, and PPI network was constructed to derive node information to prepare for the next step, which involved generating a network graph to explore the interaction between NR-related targets and ASD targets.…”