237 research outputs found

    A three-phased local search approach for the clique partitioning problem

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    This paper presents a three-phased local search heuristic CPP-P3 for solving the Clique Partitioning Problem (CPP). CPP-P3 iterates a descent search, an exploration search and a directed perturbation. We also define the Top Move of a vertex, in order to build a restricted and focused neighborhood. The exploration search is ensured by a tabu procedure, while the directed perturbation uses a GRASP-like method. To assess the performance of the proposed approach, we carry out extensive experiments on benchmark instances of the literature as well as newly generated instances. We demonstrate the effectiveness of our approach with respect to the current best performing algorithms both in terms of solution quality and computation efficiency. We present improved best solutions for a number of benchmark instances. Additional analyses are shown to highlight the critical role of the Top Move-based neighborhood for the performance of our algorithm and the relation between instance hardness and algorithm behavior

    A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

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    <p>Abstract</p> <p>Background</p> <p>Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task.</p> <p>Results</p> <p>We present an accurate modeling of dependences between genetic markers, based on a forest of hierarchical latent class models which is a particular class of probabilistic graphical models. This model offers an adapted framework to deal with the fuzzy nature of linkage disequilibrium blocks. In addition, the data dimensionality can be reduced through the latent variables of the model which synthesize the information borne by genetic markers. In order to tackle the learning of both forest structure and probability distributions, a generic algorithm has been proposed. A first implementation of our algorithm has been shown to be tractable on benchmarks describing 10<sup>5 </sup>variables for 2000 individuals.</p> <p>Conclusions</p> <p>The forest of hierarchical latent class models offers several advantages for genome-wide association studies: accurate modeling of linkage disequilibrium, flexible data dimensionality reduction and biological meaning borne by latent variables.</p

    Subnetwork Constraints for Tighter Upper Bounds and Exact Solution of the Clique Partitioning Problem

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    We consider a variant of the clustering problem for a complete weighted graph. The aim is to partition the nodes into clusters maximizing the sum of the edge weights within the clusters. This problem is known as the clique partitioning problem, being NP-hard in the general case of having edge weights of different signs. We propose a new method of estimating an upper bound of the objective function that we combine with the classical branch-and-bound technique to find the exact solution. We evaluate our approach on a broad range of random graphs and real-world networks. The proposed approach provided tighter upper bounds and achieved significant convergence speed improvements compared to known alternative methods.Comment: 20 pages, 3 figure

    Scalable kernelization for the maximum independent set problem

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    Phasage d’haplotypes par ASP à partir de longues lectures : une approche d’optimisation flexible

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    Version non corrigĂ©e. Une nouvelle version sera disponible d'ici mars 2023.Each chromosome of a di- or polyploid organism has several haplotypes, which are highly similar but diverge on a certain number of positions. However, most of the reference genomes only provide a single sequence for each chromosome, and therefore do not reflect the biological reality.Yet, it is crucial to have access to this information, which is useful in medicine, agronomy and population studies. The recent development of third generation technologies, especially PacBio and Oxford Nanopore Technologies sequencers, has allowed for the production of long reads that facilitate haplotype sequence reconstruction.Bioinformatics methods exist for this task, but they provide only a single solution. This thesis introduces an approach for haplotype phasing based on the search of connected components in a read similarity graph to identify haplotypes. This method uses Answer Set Programming to work on the set ofoptimal solutions. This phasing algorithm has been used to reconstruct haplotypes of the diploid rotifer Adineta vaga.Chaque chromosome d’organisme di- ou polyploĂŻde prĂ©sente plusieurs haplotypes, qui sont fortement similaires mais divergent sur un certain nombre de positions. Cependant, la majoritĂ© des gĂ©nomes de rĂ©fĂ©rence ne renseignent qu’une seule sĂ©quence pour chaque chromosome, et ne reflĂštent donc pas la rĂ©alitĂ© biologique. Or, il est crucial d’avoir accĂšs Ă  ces informations, qui sont utiles en mĂ©decine, en agronomie ou encore dans l’étude des populations. Le rĂ©cent dĂ©veloppement des technologies de troisiĂšme gĂ©nĂ©ration, notamment des sĂ©quenceurs PacBio et Oxford NanoporeTechnologies, a permis la production de lectures longues facilitant la reconstruction des sĂ©quences d’haplotypes. Il existe pour cela des mĂ©thodes bioinformatiques, mais elles ne fournissent qu’une unique solution. Cette thĂšse propose une mĂ©thode de phasage d’haplotype basĂ©e sur la recherchede composantes connexes dans un graph de similaritĂ© des lectures pour identifier les haplotypes. Cette mĂ©thode utilise l’Answer Set Programming pour travailler sur l’ensemble des solutions optimales. L’algorithme de phasage a permis de reconstruire les haplotypes du rotifĂšre diploĂŻde Adineta vaga
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