Markov Clustering Research Articles

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks.

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.

Hybrid Approach To Abstractive Summarization

Text summarization is an application of information retrieval where short and non-redundant version of comparatively large text is presented to the end user. In this paper a hybrid approach is presented to generate abstract summary in which sentences are clustered using sentence level relationships among sentences in association with Markov clustering principle. Then sentence ranking is done in each cluster and if possible the top weighted sentence of each cluster is fused using some linguistic rules with its best-fit sentence(if found) within that cluster to generate a new sentence. Then top ranked sentences from each cluster are compressed using classification technique to generate the abstract summary. The proposed system is evaluated with DUC 2002 data set and the result is performing better than other existing systems.

A proximity-based graph clustering method for the identification and application of transcription factor clusters

BackgroundTranscription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions.MethodsHere, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF.ResultsWe show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions.ConclusionThe interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks.

Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure. The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families. MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot. emoriyama2@unl.edu. Supplementary data are available at Bioinformatics online.

Multilevel Flow-Based Markov Clustering for Design Structure Matrices

For decomposition and integration of systems, one needs extensive knowledge of system structure. A design structure matrix (DSM) model provides a simple, compact, and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering, that is able to cope with the presence of “bus” elements, returns multilevel clusters, is capable of clustering weighted, directed, and undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters. Comparison with two algorithms from the literature shows that the proposed algorithm provides clusterings of similar quality at the expense of less central processing unit (CPU) time.

An effective approach to detecting both small and large complexes from protein-protein interaction networks

BackgroundPredicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size ≥3, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size ≤ 3) and large (size >3) complexes from PPI networks.ResultsIn this paper, we propose a novel method, called CPredictor2.0, that can detect both small and large complexes under a unified framework. Concretely, we first group proteins of similar functions. Then, the Markov clustering algorithm is employed to discover clusters in each group. Finally, we merge all discovered clusters that overlap with each other to a certain degree, and the merged clusters as well as the remaining clusters constitute the set of detected complexes. Extensive experiments have shown that the new method can more effectively predict both small and large complexes, in comparison with the state-of-the-art methods.ConclusionsThe proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes.

Cyber pirating and Detection of malicious activities p2p botnets using Markov cluster algorithm

Cyber pirating and Detection of malicious activities p2p botnets using Markov cluster algorithm

An optimal parallel implementation of Markov Clustering based on the coordination of CPU and GPU

An optimal parallel implementation of Markov Clustering based on the coordination of CPU and GPU

Investigating eLearning Research Trends in Iran via Automatic Semantic Network Generation

ABSTRACTThe purpose of this study is to investigate Iran’s eLearning research status in comparison with the world. We propose a method based on a text mining approach for extracting knowledge from Iranian published articles and generating the corresponding semantic network automatically. eLearning concepts are extracted from papers published in 6 years’ proceedings of ICeLeT, an International Conference on eLearning and eTeaching, in Iran. After extracting the domain-specific concepts, each pair of concepts get the possibility to be linked together based on co-occurrence in the articles. A weight is assigned to each edge according to the pointwise mutual information value of the pair of concepts. To identify gaps between the latest local and global research, the obtained semantic network is compared with another semantic network extracted from 6 years’ proceedings of ICALT, an International Conference on Advanced Learning Technologies. By applying a hybrid clustering algorithm on two networks based on the combination of label propagation and Markov clustering, and identifying the differences between node memberships and hubs, strengths and weaknesses of each network are demonstrated.

Detection of protein complex from protein-protein interaction network using Markov clustering

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

DECIPHERING THE ACTION MECHANISM OF INDONESIA HERBAL DECOCTION IN THE TREATMENT OF TYPE II DIABETES USING A NETWORK PHARMACOLOGY APPROACH

Objective: The aim of this research was to investigate action mechanism of Indonesia herbal decoctions in the treatment of Type 2 Diabetes (T2D) using network pharmacology approaches.Methods: Drug target profile analysis via Markov clustering was performed to identify the potent antidiabetic ingredients in the four herbs. Network target base identification of multicomponent synergy was applied to predict the ingredients synergetic effect. The multi-level and integrated target networks were contracted to identify the herbs major ingredients and their presumed targets. Further enrichment analysis and molecular docking were performed to validate network targets.Results: 278 ingredients from the four herbs were linked to antidiabetic drugs with an overall clustering success rate of 98.58% and 5 ingredient pairs had significant synergetic effects. Enrichment analysis demonstrates herbs candidate presumed targets were frequently involved in the significant biological process and pathways associated with progression of Type 2 diabetes (T2D) diseases. Finally, molecular docking validation revealed there was high binding site similarity between momordicoside F2 (78%), beta-sitosterol (67%) and cis-N-Feruloyltyramine (67%) with miglitol drug. In addition, the four ligands presented the higher binding affinity to Maltase-glucoamylase (MGA) receptor an enzyme responsible for the digestion of dietary starch to glucose.Conclusion: This study revealed the pharmacological mechanism of action of Indonesia herbal decoctions in the treatment of Type 2 diabetes. The herbs major presumed target played a significant biological role in the progression of Type 2 diabetes (T2D) while major herbal ingredients indicates the potential of curing Type 2 diabetes by inhibiting Maltase-glucoamylase (MGA) activity.

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.

Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process. A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm. Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV. These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

Sentence Clustering: A Comparative study

Clustering is one of the important steps in most of the text mining/information retrieval task like text summarization, domain identification etc. We are working on automatic abstractive text summarization where we require finding out tightly coupled sentences that could be merged and compressed to generate compact abstract summary. For our pipeline we require a clustering technique that does not take no. of clusters to be formed in advance as input.Therefore, we studied two important clustering techniques density based DBSCAN and graph based Markov Clustering Algorithm (MCL) in association with some sentence level relationships. Both the clustering techniques do not require no. of clusters to be formed in advance which is needed to generate summary of a text document without any intervention.Evaluation of sentence clustering is done using purity metric. Purity of both the sentence clustering technique is compared with baseline K-means clustering technique.MCL with some sentence level features performs better than others and fits into our pipeline.

Network Community Detection Based on the Physarum-Inspired Computational Framework.

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, a kind of slime, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost.

A network-pathway based module identification for predicting the prognosis of ovarian cancer patients.

BackgroundThis study aimed to screen multiple genes biomarkers based on gene expression data for predicting the survival of ovarian cancer patients.MethodsTwo microarray data of ovarian cancer samples were collected from The Cancer Genome Atlas (TCGA) database. The data in the training set were used to construct Reactome functional interactions network, which then underwent Markov clustering, supervised principal components, Cox proportional hazard model to screen significantly prognosis related modules. The distinguishing ability of each module for survival was further evaluated by the testing set. Gene Ontology (GO) functional and pathway annotations were performed to identify the roles of genes in each module for ovarian cancer.ResultsThe network based approach identified two 7-gene functional interaction modules (31: DCLRE1A, EXO1, KIAA0101, KIN, PCNA, POLD3, POLD2; 35: DKK3, FABP3, IRF1, AIM2, GBP1, GBP2, IRF2) that are associated with prognosis of ovarian cancer patients. These network modules are related to DNA repair, replication, immune and cytokine mediated signaling pathways.ConclusionsThe two 7-gene expression signatures may be accurate predictors of clinical outcome in patients with ovarian cancer and has the potential to develop new therapeutic strategies for ovarian cancer patients.

Evidence of extensive positive selection acting on cherry (Prunus avium L.) resistance gene analogs (RGAs)

The cherry tree (Prunus avium L.), is an important tree species which is intensively plagued by many phytopathogenic fungal species. Resistance gene analogs (RGAs) are the largest class of resistance (R) genes and are pivotal components at breeding projects, serving as useful functional markers linked to R genes. In order to assess the evolutionary pressures acting upon P. avium RGAs candidates, their 173 homologues that have previously been deposited in GenBank were mined. Their proteins were clustered according to their blast(p) similarities in 12 MCL (Markov Cluster Algorithm) tribes, resulting in unique and well supported paralogous gene groups (PGGs). The extent to which these genes exhibit evidence of adaptive, positive selective pressures, which are causing excessive fixation of non-synonymous mutations, was determined using a series of maximum likelihood analyses using the PAML package. The results postulate existence of robust evidence of positive selection, acting in almost all of the clustered PGGs across their phylogenies. Furthermore, analyses revealed that the majority of the positively selected amino acid residues sites are localized widely across these RGAs sequences. We speculate that the clustered distribution of these RGAs might also be pronounced of high birth and death genes rates with diversifying episodes acting on their NB-ARC domains, putatively affecting their ligandbinding specificities. Such evolutionary insights shed light on how these NBS-encoding RGAs in P. avium are being evolved, assigning them as the foremost surveillance mechanism against rapidly evolving fungal pathogens, and providing breeders with effective tools for fast-tracking the development of varieties with more durable resistance.

Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans

Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure. The availability of time course data has motivated researchers to develop methods, such as mixture and mixed-effects modelling approaches, that incorporate the temporal information contained in the shape of the trajectory of the data. However, there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters (some clusters are quite large and others are quite small). Here we propose two such methods, hierarchical clustering (IHC) and iterative pairwise-correlation clustering (IPC). We evaluate and compare the proposed methods to the Markov Cluster Algorithm (MCL) and the generalised mixed-effects model (GMM) using simulation studies and an application to a time course gene expression data set from a study containing human subjects who were challenged by a live influenza virus. We identify four types of temporal gene response modules to influenza infection in humans, i.e., single-gene modules (SGM), small-size modules (SSM), medium-size modules (MSM) and large-size modules (LSM). The LSM contain genes that perform various fundamental biological functions that are consistent across subjects. The SSM and SGM contain genes that perform either different or similar biological functions that have complex temporal responses to the virus and are unique to each subject. We show that the temporal response of the genes in the LSM have either simple patterns with a single peak or trough a consequence of the transient stimuli sustained or state-transitioning patterns pertaining to developmental cues and that these modules can differentiate the severity of disease outcomes. Additionally, the size of gene response modules follows a power-law distribution with a consistent exponent across all subjects, which reveals the presence of universality in the underlying biological principles that generated these modules.

Eco-modular product architecture identification and assessment for product recovery

In order to improve the efficiency of disassembly and product recovery of an abandoned product at the end-of-life stage, it is essential to develop modular product architecture by considering manufacturing and recovering processes in early product design stage. In this paper, a novel concept of a design methodology is introduced to develop eco-modular product architecture and assess the modularity of the architecture from the viewpoint of product recovery. Eco-modular product architecture contributes to enhancing product recovery processes by recycling and reusing modules without full disassembly at component or material levels. It leads to less consumption of natural resources and less landfill damage to the environment. Three sustainable modular drivers, namely, interface complexity, material similarity, and lifespan similarity, are introduced to reconstruct the modular architecture of commercial products into the eco-modular architecture. Alternatives of modular architectures are identified by Markov Cluster Algorithm based on these sustainable modular drivers and physical interconnections of the components of product architecture. To select the eco-modular architecture from these alternatives, we propose modularity assessment metrics to identify independent interactions between modules and the degrees of similarity within each module. To demonstrate the effectiveness of the proposed methodology, a case study is performed with a coffee maker.

A modified two-stage Markov clustering algorithm for large and sparse networks

BackgroundGraph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions. ObjectiveA fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer. MethodsImprovements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices. Clustering is performed in two stages: the initial connected network is processed in a sparse matrix until it breaks into isolated, small, and relatively dense subgraphs, which are then processed separately until convergence is obtained. An intelligent stopping criterion is also proposed to quit further processing of a subgraph that tends toward completeness with equal edge weights. The main advantage of this algorithm is that the necessary number of iterations is separately decided for each graph node. ResultsThe proposed algorithm was tested using the SCOP95 and large synthetic protein sequence data sets. The validation process revealed that the proposed method can reduce 3–6 times the processing time of huge sequence networks compared to previous Markov clustering solutions, without losing anything from the partition quality. ConclusionsA one-million-node and one-billion-edge protein sequence network defined by a BLAST similarity matrix can be processed with an upper-class personal computer in 100 minutes. Further improvement in speed is possible via parallel data processing, while the extension toward several million nodes needs intermediary data storage, for example on solid state drives.

Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics. In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster. In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy. In the present manuscript, we introduce a new method for the computational prediction of protein complexes by making the realistic assumption that proteins participate in multiple protein complexes and cellular functions. Our method can detect accurate and functionally homogeneous clusters.