Clique Algorithm Research Articles

Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods

BackgroundOur study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method. MethodsWe firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein–protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules. ResultsAfter removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways. ConclusionsProteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.

Detecting capital market convergence clubs

AbstractIn this study, we propose a new method to find convergence clubs that combine pairwise method of testing convergence with maximal clique algorithm. Unlike many of those already developed in the literature, this new method aims to find convergence clubs endogenously without depending on priori classifications. We use our method to study convergence among different capital markets as captured by their respective indices. Stock market convergence would indicate the absence of arbitrage opportunities in moving between the different markets as they would all present investors with similar risks. Furthermore, stock market convergence would be a precursor to GDP convergence as these economies would be bound by similar (possibly unobservable) common factors that affect long run macroeconomic performance.

Excavation of attractor modules for nasopharyngeal carcinoma via integrating systemic module inference with attract method.

The molecular mechanism of nasopharyngeal carcinoma (NPC) is poorly understood and effective therapeutic approaches are needed. This research aimed to excavate the attractor modules involved in the progression of NPC and provide further understanding of the underlying mechanism of NPC. Based on the gene expression data of NPC, two specific protein-protein interaction networks for NPC and control conditions were re-weighted using Pearson correlation coefficient. Then, a systematic tracking of candidate modules was conducted on the re-weighted networks via cliques algorithm, and a total of 19 and 38 modules were separately identified from NPC and control networks, respectively. Among them, 8 pairs of modules with similar gene composition were selected, and 2 attractor modules were identified via the attract method. Functional analysis indicated that these two attractor modules participate in one common bioprocess of cell division. Based on the strategy of integrating systemic module inference with the attract method, we successfully identified 2 attractor modules. These attractor modules might play important roles in the molecular pathogenesis of NPC via affecting the bioprocess of cell division in a conjunct way. Further research is needed to explore the correlations between cell division and NPC.

New Lower Bounds for Binary Constant-Dimension Subspace Codes

ABSTRACTLet denote the maximum cardinality of a set of k-dimensional subspaces of an n-dimensional vector space over the finite field of order q, , such that any two different subspaces have a distance of at least d. Lower bounds on can be obtained by explicitly constructing corresponding sets . When searching for such sets with a prescribed group of automorphisms, the search problem leads to instances of the maximum weight clique problem. The main focus is here on subgroups with small index in the normalizer of a Singer subgroup of . With a stochastic maximum weight clique algorithm and a systematic consideration of groups of the above mentioned type, new lower bounds on and for 8 ⩽ n ⩽ 11 are obtained.

A parallel maximum clique algorithm for large and massive sparse graphs

This paper describes BBMCPara, a new parallel exact maximum clique algorithm tailored for large and massive sparse graphs. The paper first presents a sequential algorithm BBMCSP, which builds on ideas from a leading bit-parallel published algorithm for middle-size graphs. It employs heavy pre-processing and a new sparse bitset encoding to outperform other state-of-the-art algorithms by up to several orders of magnitude over a set of real networks. BBMCPara parallelizes BBMCSP by splitting according to a preprocessing step of the latter. On a 20-core computer, it averages speedups close to an order of magnitude over real graphs of up to 3 million vertices. According to the reported results, BBMCPara appears to be the current fastest algorithm for large and massive real networks to the best of our knowledge.

Energy Efficiency of Parallel Multicore Programs

The increasing energy consumption of large-scale high performance resources raises technical and economical concerns. A reduction of consumed energy in multicore systems is possible to some extent with an optimized usage of computing and memory resources that is tailored to specific HPC applications. The essential step towards more sustainable consumption of energy is its reliable measurements for each component of the system and selection of optimally configured resources for specific applications. This paper briefly surveys the current approaches for measuring and profiling power consumption in large scale systems. Then, a practical case study of a real-time power measurement of multicore computing system is presented on two real HPC applications: maximum clique algorithm and numerical weather prediction model. We assume that the computing resources are allocated in a HPC cloud on a pay-per-use basis. The measurements demonstrate that the minimal energy is consumed when all available cores (up to the scaling limit for a particular application) are used on their maximal frequencies and with threads binded to the cores.

Lessons from the Congested Clique applied to MapReduce

The main result of this paper is a simulation algorithm which, under quite general constraints, transforms algorithms running in the Congested Clique model into algorithms running in the MapReduce model. As a case study of applying this simulation algorithm, we first present a distributed O(Δ)-coloring algorithm running on the Congested Clique in O(1) rounds, if Δ≥log2⁡n, and O(log⁡log⁡log⁡n) rounds otherwise. Applying the simulation theorem to this Congested Clique O(Δ)-coloring algorithm yields an O(1)-round O(Δ)-coloring algorithm in the MapReduce model. We apply our simulation algorithm to other Congested Clique algorithms including the 2-ruling set and metric facility location algorithms of Berns et al. (ICALP 2012).Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the CONGEST model), the major impediment to constructing fast algorithms is the O(log⁡n) restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models.

A new exact maximum clique algorithm for large and massive sparse graphs

This paper describes a new very efficient branch-and-bound exact maximum clique algorithm BBMCSP, designed for large and massive sparse graphs which appear frequently in real life problems from different fields.State-of-the-art exact maximum clique algorithms encode the adjacency matrix in full but when dealing with sparse graphs some form of compression is required. The new algorithm is based on a leading bit-parallel non-sparse solver but employs a novel sparse encoding for the adjacency matrix. Moreover, it also improves on recent optimizations proposed in literature for the sparse case such as core-based bounds.Reported results show that it is several orders of magnitude better than state-of-the-art. Moreover, a number of real networks with many millions of nodes are solved in a few seconds.

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

We developed LiSiCA (ligand similarity using clique algorithm)--ligand-based virtual screening software that uses a fast maximum clique algorithm to find two- and three-dimensional similarities between pairs of molecules and applied it to the discovery of novel potent butyrylcholinesterase inhibitors. LiSiCA, which runs in parallel on multiple processor cores, was successfully tested on the Database of Useful Decoys-Enhanced, to evaluate its ability to discriminate active molecules from decoys. We then applied LiSiCA for the discovery of novel inhibitors of human butyrylcholinesterase, a promising anti-Alzheimer target, using a known inhibitor as the reference compound. We demonstrated that LiSiCA is capable of finding potent nanomolar inhibitors, whose scaffolds differed from the reference compound, thus proving its ability for scaffold hopping and usefulness in drug discovery.

Parallel Maximum Clique Algorithms with Applications to Network Analysis

We present a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from a thousand to a hundred million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. At its heart the algorithm employs a branch-and-bound strategy with novel and aggressive pruning techniques. The pruning techniques include the combined use of core numbers of vertices along with a good initial heuristic solution to remove the vast majority of the search space. In addition, the exploration of the search tree is parallelized. During the search, processes immediately communicate changes to upper and lower bounds on the size of the maximum clique. This exchange of information occasionally results in a superlinear speedup because tasks with large search spaces can be pruned by other processes. We demonstrate the impact of the algorithm on applications using two different network analysis problems: computation of temporal strong components in dynamic networks and determination of compression-friendly ordering of nodes of massive networks.

탄성변형에너지 측도를 이용한 부분적으로 가려진 이진 객체의 인식

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line ∙제1저자 : 문영인 ∙교신저자 : 구자영 ∙투고일 : 2014. 8. 20, 심사일 : 2014. 9. 5, 게재확정일 : 2014. 9. 27. * 단국대학교 컴퓨터학과 졸업(Dept. of Computer Science and Engineering, Dankook University) ** 단국대학교 소프트웨어학과(Dept. of Software Science, Dankook University) 64 Journal of The Korea Society of Computer and Information October 2014 segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate. ▸

Predicting protein complex in protein interaction network - a supervised learning based method

BackgroundProtein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, making it possible to predict protein complexes from protein -protein interaction networks. However, most of current methods are unsupervised learning based methods which can't utilize the information of the large amount of available known complexes.MethodsWe present a supervised learning-based method for predicting protein complexes in protein - protein interaction networks. The method extracts rich features from both the unweighted and weighted networks to train a Regression model, which is then used for the cliques filtering, growth, and candidate complex filtering. The model utilizes additional "uncertainty" samples and, therefore, is more discriminative when used in the complex detection algorithm. In addition, our method uses the maximal cliques found by the Cliques algorithm as the initial cliques, which has been proven to be more effective than the method of expanding from the seeding proteins used in other methods.ResultsThe experimental results on several PIN datasets show that in most cases the performance of our method are superior to comparable state-of-the-art protein complex detection techniques.ConclusionsThe results demonstrate the several advantages of our method over other state-of-the-art techniques. Firstly, our method is a supervised learning-based method that can make full use of the information of the available known complexes instead of being only based on the topological structure of the PIN. That also means, if more training samples are provided, our method can achieve better performance than those unsupervised methods. Secondly, we design the rich feature set to describe the properties of the known complexes, which includes not only the features from the unweighted network, but also those from the weighted network built based on the Gene Ontology information. Thirdly, our Regression model utilizes additional "uncertainty" samples and, therefore, becomes more discriminative, whose effectiveness for the complex detection is indicated by our experimental results.

Algorithmic tools for tripartite data analysis

Materials and methods In this work, tripartite graphs are considered. Applications include comparing two sets of many gene-many disease associations. An algorithm is described that finds a maximum triclique in such a graph. It employs a branching strategy inspired by maximum clique algorithms for general graphs. A binary search tree is used, in which branch nodes in the tree represent vertices in the tripartite graph, and in which branching decisions are based on whether a vertex is in or out of a maximum triclique. A reduction rule is also introduced to filter out irrelevant vertices. This algorithm was developed in the context of GeneWeaver, an online system for the integration of functional genomics experimental results. In this system triclique extraction will enable fast transitive association of diseases based on the similarity of gene-disease associations from many experiments. Computational experience with huge volumes of experimental data is described.

Chemical Structure Elucidation from 13C NMR Chemical Shifts: Efficient Data Processing Using Bipartite Matching and Maximal Clique Algorithms

Computer-assisted chemical structure elucidation has been intensively studied since the first use of computers in chemistry in the 1960s. Most of the existing elucidators use a structure-spectrum database to obtain clues about the correct structure. Such a structure-spectrum database is expected to grow on a daily basis. Hence, the necessity to develop an efficient structure elucidation system that can adapt to the growth of a database has been also growing. Therefore, we have developed a new elucidator using practically efficient graph algorithms, including the convex bipartite matching, weighted bipartite matching, and Bron-Kerbosch maximal clique algorithms. The utilization of the two matching algorithms especially is a novel point of our elucidator. Because of these sophisticated algorithms, the elucidator exactly produces a correct structure if all of the fragments are included in the database. Even if not all of the fragments are in the database, the elucidator proposes relevant substructures that can help chemists to identify the actual chemical structures. The elucidator, called the CAST/CNMR Structure Elucidator, plays a complementary role to the CAST/CNMR Chemical Shift Predictor, and together these two functions can be used to analyze the structures of organic compounds.

Minimal join graph set synthesis proprietor possession cliques algorithm

The effective minimal join graph set synthesis algorithm (of self-managing cliques)from a given maximal knowledge pattern set and two its improvements, each of which is actualized in an algorithm, exists, however there is no algorithm that implements the two improvements. The goal of the work is to designing a new algorithm that actualizes all known improvements of the basic algorithm, and thus, is the most effective one. Such an algorithm implementing suggested improvement is designed and correctness of its work is proven.algebraic Bayesian networksalgebraic Bayesian networks

Minimal join graph set synthesis self-managed possession cliques algorithm

The effective minimal join graph set synthesis algorithm (of self-managing cliques) from a given maximal knowledge pattern set exists, however it can be improved by engaging results of theory of algebraic Bayesian network global structure that is under development. The goal of the work is to improve the known minimal join graph set algorithm by perfecting designing set of vertices that cliques include: instead of full search of all the pairs of a vertex and a clique weight, particular search for each clique its descendants is suggested. A new algorithm of self-managed possessions cliques implementing suggested improvement is designed.

Minimal join graph set synthesis possession cliques algorithm

The effective minimal join graph set synthesis algorithm (of self-managing cliques) from a given maximal knowledge pattern set is known, but it can be improved by engaging the developed theory of algebraic Bayesian network global structure. The goal of the work is to improve the known minimal join graph set algorithm by perfecting designing possessions (connection components of strong narrowings) — the key objects for the set synthesis: to design themnot with the straight search but with the corresponding cliques children’s intersections analysis. A newalgorithmimplementing suggested improvements is designed.

Minimal join graph set synthesis self-managing cliques algorithm

The scheme of an algorithm allowing to design minimal join graph set from a given maximal knowledge pattern set is known, but it can be improved by engaging the developed theory of algebraic Bayesian network global structure. The goal of the research is to improve the known minimal join graph set algorithm. Three improvements of the known algorithm are suggested and justified: 1) exclusion of not significant narrowing, 2) exclusion of cliques with the only possession and 3) a prior childless cliques with the only edge processing. A new algorithm implementing suggested improvements is designed.

Maximum Clique Algorithm and Its Approximation for Uniform Test Form Assembly

Educational assessments occasionally require uniform test forms for which each test form comprises a different set of items, but the forms meet equivalent test specifications (i.e., qualities indicated by test information functions based on item response theory). We propose two maximum clique algorithms (MCA) for uniform test form assembly. The proposed methods can assemble uniform test forms with allowance of overlapping items among uniform test forms. First, we propose an exact method that maximizes the number of uniform test forms from an item pool. However, the exact method presents computational cost problems. To relax those problems, we propose an approximate method that maximizes the number of uniform test forms asymptotically. Accordingly, the proposed methods can use the item pool more efficiently than traditional methods can. We demonstrate the efficiency of the proposed methods using simulated and actual data.

Multi-Threading a State-of-the-Art Maximum Clique Algorithm

We present a threaded parallel adaptation of a state-of-the-art maximum clique algorithm for dense, computationally challenging graphs. We show that near-linear speedups are achievable in practice and that superlinear speedups are common. We include results for several previously unsolved benchmark problems.