A novel functional module detection algorithm for protein-protein interaction networks

Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

The identification and analysis of functional modules in protein–protein interaction (PPI) networks provide insight into understanding the organization and function of biological systems. A lot of overlapping structures are shared by the functional modules in PPI networks, which indicates there are some proteins play indispensable roles in different biological processes. Markov clustering (MCL) is a popular algorithm for clustering networks in bioinformatics. In this paper, to identify the overlapping structures among the functional modules and find more modules with biological significance in PPI networks, we propose a Markov clustering algorithm based on link similarity (MLS). First of all, the weighted link similarity is calculated and the link similarity matrix which measures the association strength of the protein interactions can be gotten. Then, the link similarity matrix is divided by applying Markov clustering, and the clustering results are mapped to original networks to analyze the protein modules. The method has been experimented on three databases, including DIP, Gavin and Krogan. Our results show that the MLS cannot only accurately identify the functional modules, but also outperform the original MCL algorithm and the F-measure value improved 5–10% compared with it.

HAM-FMD: Mining functional modules in protein–protein interaction networks using ant colony optimization and multi-agent evolution

BackgroundStudies of functional modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in detecting functional modules.ResultsWe present a new approach using multi-agent evolution for detection of functional modules in PPI networks. The proposed approach consists of two stages: the solution construction for agents in a population and the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the detection problem of functional modules in a PPI network. First, the approach utilizes a connection-based encoding scheme to model an agent, and employs a random-walk behavior merged topological characteristics with functional information to construct a solution. Next, it applies several evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents as well as solution evolution. Systematic experiments have been conducted on three benchmark testing sets of yeast networks. Experimental results show that the approach is more effective compared to several other existing algorithms.ConclusionsThe algorithm has the characteristics of outstanding recall, F-measure, sensitivity and accuracy while keeping other competitive performances, so it can be applied to the biological study which requires high accuracy.

Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities.

Functional module identification in a Protein-Protein Interaction (PPI) network is one of the most important and challenging tasks in computational biology. For detecting functional modules, it is difficult to solve the problem directly and always results in a low accuracy and a large discard rate. In this paper, we present a novel algorithm of ant colony optimization with multi-agent evolution for detecting functional modules. The proposed ACO-MAE algorithm enhances the performance of ant colony optimization (ACO) by incorporating multi-agent evolution (MAE). First, the ant colony optimization for solving Traveling Salesman Problems (TSP) is conducted to construct primary clustering results. Then, the multi-agent evolutionary process is performed to move out of local optima. From simulation results, it is shown that the proposed ACO-MAE algorithm has superior performance when compared to other existing algorithms.

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN’s community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Motivation: The identification of functional modules from protein–protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with “core” and “peripheral” proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level.Results: We propose NCMine, which is a novel network clustering method and visualization tool for the core-peripheral structure of functional modules. It extracts near-complete subgraphs from networks based on a node-weighting scheme using degree centrality, and reports subgroups as functional modules. We implemented this method as a plugin of Cytoscape, which is widely used to visualize and analyze biological networks. The plugin allows users to extract functional modules from PPI networks and interactively filter modules of interest. We applied the method to human PPI networks, and found several examples with the core-peripheral structure of modules that may be related to cancer development.Availability and Implementation: The Cytoscape plugin and tutorial are available at Cytoscape AppStore. (http://apps.cytoscape.org/apps/ncmine).Contact:kengo@ecei.tohoku.ac.jpSupplementary information:Supplementary data are available at Bioinformatics online.

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks.

BackgroundBiological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow.ResultsWe devised a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently find clusters or functional modules in complex biological networks as well as hubs and outliers. More specifically, we demonstrated that we can find functional modules in complex networks and classify nodes into various roles based on their structures. In this study, we showed the effectiveness of our methodology using the budding yeast (Saccharomyces cerevisiae) protein-protein interaction network. To validate our clustering results, we compared our clusters with the known functions of each protein. Our predicted functional modules achieved very high purity comparing with state-of-the-art approaches. Additionally the theoretical and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest approach for networks.ConclusionWe compare our algorithm with well-known modularity based clustering algorithm CNM. We successfully detect functional groups that are annotated with putative GO terms. Top-10 clusters with minimum p-value theoretically prove that newly proposed algorithm partitions network more accurately then CNM. Furthermore, manual interpretations of functional groups found by SCAN show superior performance over CNM.

Identifying functional modules in protein-protein interaction (PPI) networks is fundamental to understand cellular organization, processes, and functions. As an emerging evolutionary computational technology, swarm intelligence approaches are now becoming a new research hotspot in identifying functional modules. This paper proposes a new computational approach based on bacterial biological mechanisms for functional module detection in PPI networks (called as BBM-FMD). In BBM-FMD, each bacterium is first initialized to a candidate module partition by a random walk behavior. Then four biological mechanisms of bacteria including chemotaxis, conjugation, reproduction, and elimination and dispersal are simulated to iteratively search for better protein module partitions. At last, two post-processing steps are carried out to refine the obtained module partition. The experimental results on two PPI datasets demonstrate the superior performance of BBM-FMD in detecting functional modules compared with several other algorithms.

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes and differ based on the composition, affinity and lifetime of the association. A vast amount of PPI data for various organisms is available from MIPS, DIP and other sources. The identification of functional modules in PPI network is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins. In this paper, we propose using functional flow simulation and the topology of the network for the functional module detection and function prediction problem. Our approach is based on the functional influence model that quantifies the influence of a biological component on another. We introduce a flow simulation algorithm to generate a functional profile for each component. In addition, a new clustering method FMD (Functional Module Detection) is designed to associate with functional profiles to detect functional modules. We evaluate the proposed technique on three different yeast networks with MIPS functional categories and compare it with several other existing techniques in terms of precision and recall. Our experiments show that our approach achieves better accuracy than other existing methods.

Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules-groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is likely through capturing the biologically meaningful interactions. In recent years, many algorithms have been developed for detecting such structures. These algorithms, however, are computationally demanding, which limits their applications. In this paper, we propose a fast iterative-clique percolation method (ICPM) for identifying overlapping functional modules in protein-protein interaction (PPI) networks. Our method is based on clique percolation method (CPM), and it not only considers the degree of nodes to minimize the search space (the vertices in k-cliques must have the degree of k − 1 at least), but also converts k-cliques to (k − 1)-cliques. It finds k-cliques by appending one node to (k − 1)-cliques. By testing our method on PPI networks, our analysis of the yeast PPI network suggests that most of these modules have well-supported biological significance.

Although the main treatments of inflammatory bowel disease (IBD) aim at the induction and maintenance of clinical remission, several studies have demonstrated inflammation still present in clinical remission. The goal of this study was to analyze the gene expression profiles between the pediatric IBD and control samples, aiming to further investigate the underlying therapeutic target in remission. Gene expression profiles data of GSE33943 were downloaded from Gene Expression Omnibus, which included 45 pediatric IBD samples and 13 control samples. The differentially expressed genes (DEGs) between IBD and control samples were identified by LIMMA package in R and the function of DEGs were predicted by Gene Ontology and KEGG pathway enrichment analyses using GeneAnswer package. Furthermore, a protein-protein interaction (PPI) network was constructed through the STRING database and functional module was obtained using ClusterONE. A total of 224 DEGs were screened between IBD and control samples. These DEGs (e.g. up-regulated FAS and down-regulated CCL5) were mainly enriched in cytokine‒cytokine receptor interaction and chemokine signaling pathway. In addition, some hub genes (e.g. up-regulated PSMA2 and PSMA6) were obtained through PPI network and functional module. These two genes were involved in Proteasome alpha-subunit and conserved site by functional module analysis. The immune and Proteasome mechanisms are still active during remission and FAS, PMSF6 and PMSF2 may be underlying targets for therapy of this disease.