@article{OmranianNikoloski2022,
  author    = {Omranian, Sara and Nikoloski, Zoran},
  title     = {CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs},
  series = {Applied Network Science},
  volume    = {7},
  journal   = {Applied Network Science},
  publisher = {Springer International Publishing},
  address   = {Cham},
  issn      = {2364-8228},
  doi       = {10.1007/s41109-022-00508-5},
  pages     = {12},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@misc{OmranianNikoloski2022,
  author    = {Omranian, Sara and Nikoloski, Zoran},
  title     = {CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1315},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-58686},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586863},
  pages     = {12},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}