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OCG / Overlapping Cluster Generator
A clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein-protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks.
PPSampler / Proteins' Partition Sampler
Predicts protein complex by sampling. PPSampler is based on the Metropolis-Hastings algorithm, that employs a Markov chain Monte Carlo (MCMC) method. It replaces the sum of the weights of protein-protein interactions (PPIs) within a cluster with a generalized density of the cluster. This tool is able to construct samples from particular probability distributions. It was evaluated thanks to precision, recall, and F-measure applied on predicted clusters.
SPIN / Simultaneous Protein Interaction Network
A network refinement model based on the structural interface data of protein pairs for protein complex predictions. A simple PPI network, which is represented as a static entity, includes competitive interactions that cannot participate in complex formations together. In the proposed framework, a SPIN construction reserves sets of non-competitive interactions by considering mutual exclusions among the interactions in a network. This allows network-clustering algorithms to identify stable clusters that may possibly be matched by to actual protein complexes.
ProCope / Protein complex Prediction and Evaluation
Allows prediction and evaluation of protein complexes from purification datasets which integrates efficient implementations of the major prediction methods. ProCope can be useful for both applying published methods on new datasets to obtain reproducible and predictions and for developing and evaluating new prediction methods. The software provides a graphical user interface (GUI), command line tools suitable for batch job processing and a Java application programming interface (API). The GUI can also be used as a Cytoscape plugin.
Parses the PSI-25 files generated by the IntAct data repository which collects, curates and stores thousands of protein interactions. Rintact is an R package which provides two main functions: (i) psi25interaction that takes either a PSI-25 XML file from IntAct or an URL containing the web address of where such an XML file can be obtained and (ii) psi25complex, also takes a PSI-25 XML file or URL as an input parameter, but the file must contain protein complex membership information.
NEOComplex / NECC- and Ortholog-based Complex identification by multiple network alignment
Recognizes protein complexes by appending multiple network alignments MNAs. NEOComplex is able to discover some biological examples that cannot be found by conventional complex identification tools. It can achieve good balance in precision and sensitivity. This tool includes the result of an arbitrary MNA algorithm as an input to provide orthology information. It permits users to identify particularly sparse protein complexes.
MCODE / Molecular Complex Detection
A graph theoretic clustering algorithm that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks.
An algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.
TINCD / Two-layer INtegrative Complex Detection
A two-layer integration framework to identify protein complexes. First, TINCD constructs consensus matrices for proteins and measures their co-complex propensity based on the complex knowledge discovered by various graph clustering results. Second, a similarity network fusion (SNF) strategy is employed by TINCD to combine consensus matrices and score matrix obtained from TAP data to obtain a final co-complex score matrix. Finally, a novel graph regularized doubly stochastic matrix decomposition model is proposed to detect overlapping protein complexes from the final score matrix.
MAE-FMD / Multi-Agent Evolutionary method for Functional Module Detection
Detects functional modules in protein-protein interaction (PPI) networks. MAE-FMD employs a group of agents as a population to carry out random walks from a start protein to other proteins in a PPI network and finish their individual solution encodings. It randomly places these agents into an evolutionary environment modeled as a lattice, and performs innovative agent-based operations such as competition, cooperation, and mutation.
NBC-HD-PCP / Naïve Bayes Classifier for HeteroDimeric Protein Complex Prediction
Predicts heterodimeric protein complexes. NBC-HD-PCP is based on a naïve Bayes classifier that uses different features, such as protein-protein interaction (PPI) data, gene ontology annotations, and protein localization data, for heterodimeric protein complexes. It employs a support vector machine (SVM) method. This tool was evaluated in a five-fold cross-validation on the prediction of the class of the pair of the proteins of each of the PPIs.
ACC-FMD / Ant Colony Clustering for Functional Module Detection
Allows detection of functional modules. ACC-FMD is an algorithm adopting an ant colony clustering model to mine functional modules in protein-protein interaction (PPI) networks. First, it calculates the clustering coefficient of each node and selects some proteins with the higher clustering coefficients as ant seed nodes. And next, it develops the picking and dropping probability models to induct the ant colony clustering.
A coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested RANCoC on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.
PINCoC / PPI network Co-Clustering
Searches for functional modules in protein-protein interaction (PPI) networks. PINCoC is a software based on a co-clustering approach that has two advantages: the number of clusters is automatically determined by the algorithm and the problem of ties occurring in protein-protein distances plaguing algorithms based on hierarchical clustering is implicitly solved. Tests carried out on S. cerevisiae proteins showed that the method returns biologically relevant partitions, correctly clustering proteins which are known to be involved in different biological processes.
BFO-FMD / Bacterial Foraging Optimization for Functional Module Detection
Serves functional module detection in PPI networks. BFO-FMD works in three stages: solution initialization, solution optimization, and post-processing. First, it constructs each bacterial individual solution using a random-walk behavior. Next, it optimizes iteratively each individual solution using four biological mechanisms to look for superior solutions with higher modularity scores. And finally, it carries out two post-processing steps to further refine the preliminary module partition detected.
Allows identification of functional modules in protein-protein interaction (PPI) networks. HAM-FMD is based on ant colony optimization and multi-agent evolution (ACO-MAE). This tool focuses on the detection of densely connected subgraphs within the graphic representation of a PPI network. It proceeds to a transformation of the clustering of proteins in a PPI network into the searching of an optimization path among proteins by a neighboring distance metric. Then, some optimal paths with high quality were encoded and evolved in a multi-agent evolutionary environment.
A clustering algorithm based on a new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. The results have shown that IPCA is robust against the high rate of false positives and false negatives in the protein interaction networks. IPCA can thus be used to identify new protein complexes in protein interaction networks of various species and to provide references for biologists in their research on protein complexes.
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