Parallel swarm intelligence strategies for large-scale clustering based on MapReduce with application to epigenetics of aging

Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, it becomes a challenging issue due to the huge amount of data recently collected making conventional clustering algorithms inappropriate. The use of swarm intelligence algorithms has shown promising results when applied to data clustering of moderate size due to their decentralized and self-organized behavior. However, these algorithms exhibit limited capabilities when large data sets are involved. In this paper, we developed a decentralized distributed big data clustering solution using three swarm intelligence algorithms according to MapReduce framework. The developed framework allows cooperation between the three algorithms namely particle swarm optimization, ant colony optimization and artificial bees colony to achieve largely scalable data partitioning through a migration strategy. This latter reaps advantage of the combined exploration and exploitation capabilities of these algorithms to foster diversity. The framework is tested using amazon elastic map-reduce service (EMR) deploying up to 192 computer nodes and 30 gigabytes of data. Parallel metrics such as speed-up, size-up and scale-up are used to measure the elasticity and scalability of the framework. Our results are compared with their counterparts big data clustering results and show a significant improvement in terms of time and convergence to good quality solution. The developed model has been applied to epigenetics data clustering according to methylation features in CpG islands, gene body, and gene promoter in order to study the epigenetics impact on aging. Experimental results reveal that DNA-methylation changes slightly and not aberrantly with aging corroborating previous studies

Symptom-based stratification of patients with primary Sjögren's syndrome: multi-dimensional characterisation of international observational cohorts and reanalyses of randomised clinical trials

Background: Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. / Methods: We did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. / Findings: In the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo. / Interpretation: Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases. / Funding: UK Medical Research Council, British Sjogren's Syndrome Association, French Ministry of Health, Arthritis Research UK, Foundation for Research in Rheumatology

SOSORT 2012 consensus paper: reducing x-ray exposure in pediatric patients with scoliosis

This 2012 Consensus paper reviews the literature on side effects of x-ray exposure in the pediatric population as it relates to scoliosis evaluation and treatment. Alternative methods of spinal assessment and imaging are reviewed, and strategies for reducing the number of radiographs are developed. Using the Delphi technique, SOSORT members developed consensus statements that describe how often radiographs should be taken in each of the pediatric and adolescent sub-populations

Symptom-based stratification of patients with primary Sjögren's syndrome: multi-dimensional characterisation of international observational cohorts and reanalyses of randomised clinical trials

Background Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. Methods We did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. Findings In the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo. Interpretation Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases. Funding UK Medical Research Council, British Sjogren's Syndrome Association, French Ministry of Health, Arthritis Research UK, Foundation for Research in Rheumatology

A novel quantum behaved Particle Swarm optimization algorithm with chaotic search for image alignment.

In this paper we investigate the use of Artificial Immune Systems’ principles to cope with the satisfiability problem. We describe ClonSAT, a new iterative approach for solving the well known Maximum Satisfiability (Max-SAT) problem. This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The underlying idea is to harness the optimization capabilities of artificial clonal selection algorithm to achieve good quality solutions for MaxSAT problem. To foster the process, a local search has been used. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed hybrid approach.King Saud Universit

Quantum Genetic Algorithm for Multiple RNA Structural Alignment

RNA structural alignment is one of key issues in bioinformatics. It aims to elucidate conserved structural regions among a set of sequences. Finding an accurate conserved structure is still difficult and a time consuming task that involves structural alignment as a prerequisite. In this work, structural alignment is viewed as an optimization process. A quantum based genetic algorithm is proposed to carry out this process. The main features of this algorithm consist in the quantum structure used to represent alignments and the quantum operators defining the overall evolutionary dynamic of the genetic algorithm. The quantum structure relies on the concept of qubit and allows efficient encoding of individuals. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to achieve good quality solutions

Towards the best points of interpolation using particles swarm optimisation approach

Many studies, mostly empirical, have been devoted to finding optimal choice nodes for interpolating. When exploring the underlying factors that determine what is a good such choice, we are led to consider the Runge phenomenon (RP; best known in case of high order polynomial interpolation) as a key error mechanism. However, we shall see by using PSO that interpolation with polynomials of high degree can be very useful for the obtaining of the estimates of functions. This paper presents the advantage of using the Particle Swarm Optimization (PSO) interpolation nodes. PSO is a technique based on the cooperation between particles. The exchange of information between these particles allows to resolve difficult problem

A multiresolution approach based on MRF and Bak-Sneppen models for image segmentation

The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase. In this paper, we combine Bak–Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak–Sneppen model. The a-posteriori probability corresponds to a local fitness. At each cycle, some objectionable species are chosen for a random change in their fitness values. Furthermore, the change in the fitness of each species engenders fitness changes for its neighboring species. After a certain number of iteration, the system converges to a Maximum A Posteriori estimate. In this multireolution approach, we use a wavelet transform to reduce the size of the system