TY  - JOUR
A1  - Omranian, Sara
A1  - Nikoloski, Zoran
T1  - CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs
JF  - Applied Network Science
N2  - High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor.
KW  - Protein complexes
KW  - Protein–protein interaction
KW  - Network clustering
KW  - Species comparison
Y1  - 2022
U6  - https://doi.org/10.1007/s41109-022-00508-5
SN  - 2364-8228
VL  - 7
PB  - Springer International Publishing
CY  - Cham
ER  -