Detecting protein complexes in a PPI network: a gene ontology based multi-objective evolutionary approach.

@article{Mukhopadhyay2012DetectingPC,
  title={Detecting protein complexes in a PPI network: a gene ontology based multi-objective evolutionary approach.},
  author={A. Mukhopadhyay and Sumanta Ray and Moumita De},
  journal={Molecular bioSystems},
  year={2012},
  volume={8 11},
  pages={
          3036-48
        }
}
Protein complexes play an important role in cellular mechanism. Identification of protein complexes in protein-protein interaction (PPI) networks is the first step in understanding the organization and dynamics of cell function. Several high-throughput experimental techniques produce a large amount of protein interactions, which can be used to predict protein complexes in a PPI network. We have developed an algorithm PROCOMOSS (Protein Complex Detection using Multi-objective Evolutionary… 
View on PubMed
researchgate.net
46 Citations
Highly Influential Citations
1
Background Citations
8
Methods Citations
18

46 Citations

Citation Type

Citation Type

Protein Complex Prediction in Large Ontology Attributed Protein-Protein Interaction Networks
TLDR
This paper constructed ontology attributed PPI networks with PPI data and GO resource and proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity), which showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.
Incorporating fuzzy semantic similarity measure in detecting human protein complexes in PPI network: A multiobjective approach
TLDR
This work has developed a multi-objective algorithm for detecting human protein complexes by partitioning large human PPI network into clusters which serve as protein complexes using a fuzzy measure based semantic similarity approach.
Protein Complex Detection in PPI Network by Identifying Mutually Exclusive Protein-protein Interactions☆
A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks
  • Duc-Hau Le
  • Computer Science
    Algorithms for Molecular Biology
  • 2015
TLDR
The proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.
Integrating network topology, gene expression data and GO annotation information for protein complex prediction
TLDR
A new method is proposed, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks.
Protein complexes predictions within protein interaction networks using genetic algorithms
TLDR
A new clustering algorithm for predicting protein complexes through the use of genetic algorithms that surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes.
Detection of protein complexes from multiple protein interaction networks using graph embedding
Disease associated protein complex detection: A multi-objective evolutionary approach
TLDR
Density and disease associations are to be maximized while outward interactions are minimized here and MODAPROC is fast, robust, and provides a good analysis tool for finding and discovering obscure disease specific gene clusters and can be extended using other domain specific objective functions.
WCOACH: Protein complex prediction in weighted PPI networks.
TLDR
A semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks, and one of the well-known methods, COACH, has been improved to be compatible with weighted PPI Networks for protein complex detection.
Novel Gene Ontology Based Distance Metric for Function Prediction via Clustering in Protein Interaction Networks
TLDR
It is shown that the rise of function prediction performance when using the novel distance metric for PIN clustering is significant, as compared to traditional approaches.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 48 REFERENCES
Development and implementation of an algorithm for detection of protein complexes in large interaction networks
TLDR
The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units and can help to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.
Functional topology in a network of protein interactions
TLDR
The authors' models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways.
Evaluation of clustering algorithms for protein-protein interaction networks
TLDR
The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks, and shows that MCL is remarkably robust to graph alterations.
Detecting overlapping protein complexes in protein-protein interaction networks
TLDR
ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence catalog and complexes derived from the Saccharomyces Genome Database than the results of seven popular methods.
An automated method for finding molecular complexes in large protein interaction networks
TLDR
A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
MPact: the MIPS protein interaction resource on yeast
TLDR
The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of curated data with high-throughput datasets.
DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions
The Database of Interacting Proteins (DIP: http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. It provides the scientific community with…
Protein complexes and functional modules in molecular networks
  • V. Spirin, L. Mirny
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 2003
TLDR
The results provide strong support for the network modularity principle introduced by Hartwell et al. (1999), suggesting that found modules constitute the “building blocks” of molecular networks.
Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships
TLDR
The feasibility of applying gene ontology-driven similarity methods to functional prediction tasks, such as the validation of gene expression analyses and the identification of false positives in protein interaction studies, is supported.
Detection of functional modules from protein interaction networks
TLDR
This study successfully isolate 1046 functional modules from the known protein interaction network of Saccharomyces cerevisiae involving 8046 individual pair‐wise interactions by using an entirely automated and unsupervised graph clustering algorithm.
...
1
2
3
4
5
...