ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Clustering of Schistosoma mansoni mRNA sequences and analysis of the most transcribed genes: implications in metabolism and biology of different developmental stages

The study of the Schistosoma mansoni genome, one of the etiologic agents of human schistosomiasis, is essential for a better understanding of the biology and development of this parasite. In order to get an overview of all S. mansoni catalogued gene sequences, we performed a clustering analysis of the parasite mRNA sequences available in public databases. This was made using softwares PHRAP and CAP3. The consensus sequences, generated after the alignment of cluster constituent sequences, allowed the identification by database homology searches of the most expressed genes in the worm. We analyzed these genes and looked for a correlation between their high expression and parasite metabolism and biology. We observed that the majority of these genes is related to the maintenance of basic cell functions, encoding genes whose products are related to the cytoskeleton, intracellular transport and energy metabolism. Evidences are presented here that genes for aerobic energy metabolism are expressed in all the developmental stages analyzed. Some of the most expressed genes could not be identified by homology searches and may have some specific functions in the parasite.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

STACK: Sequence Tag Alignment and Consensus Knowledgebase

STACK is a tool for detection and visualisation of expressed transcript variation in the context of developmental and pathological states. The datasystem organises and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index and is accessible via WWW at http://www.sanbi.ac.za/Dbases.html. STACK represents a broadly applicable resource, as it is the only reconstructed transcript database for which the tools for its generation are also broadly available (http://www.sanbi.ac.za/CODES).

Travelers’ use of social media: A clustering approach

Research regarding the use of social media among travelers has mainly focused on its impact on travelers’ travel planning process and there is consensus that travel decisions are highly influenced by social media. Yet, little attention has been paid to the differences among travelers regarding their use of social media for travel purposes. Based on the use of travel social media, cluster analysis was employed to identify different segments among travelers. Furthermore, the study profiles the clusters based on demographic and other travel related characteristics. The findings of this study are important to online marketers to better understand traveler’s use of social media and their characteristics, in order to adapt online marketing strategies according to the profile of each segment.

Travelers’ use of social media: A clustering approach

Research regarding the use of social media among travelers has mainly focused on its impact on travelers’ travel planning process and there is consensus that travel decisions are highly influenced by social media. Yet, little attention has been paid to the differences among travelers regarding their use of social media for travel purposes. Based on the use of travel social media, cluster analysis was employed to identify different segments among travelers. Furthermore, the study profiles the clusters based on demographic and other travel related characteristics. The findings of this study are important to online marketers to better understand traveler’s use of social media and their characteristics, in order to adapt online marketing strategies according to the profile of each segment.

[renal Tumors: The International Society Of Urologic Pathology (isup) 2012 Consensus Conference Recommendations].

During the last 30 years many advances have been made in kidney tumor pathology. In 1981, 9 entities were recognized in the WHO Classification. In the latest classification of 2004, 50 different types have been recognized. Additional tumor entities have been described since and a wide variety of prognostic parameters have been investigated with variable success; however, much attention has centered upon the importance of features relating to both stage and grade. The International Society of Urological Pathology (ISUP) recommends after consensus conferences the development of reporting guidelines, which have been adopted worldwide ISUP undertook to review all aspects of the pathology of adult renal malignancy through an international consensus conference to be held in 2012. As in the past, participation in this consensus conference was restricted to acknowledged experts in the field.

Clustering Of Water Bodies In Unpolluted And Polluted Environments Based On Escherichia Coli Phylogroup Abundance Using A Simple Interaction Database.

Different types of water bodies, including lakes, streams, and coastal marine waters, are often susceptible to fecal contamination from a range of point and nonpoint sources, and have been evaluated using fecal indicator microorganisms. The most commonly used fecal indicator is Escherichia coli, but traditional cultivation methods do not allow discrimination of the source of pollution. The use of triplex PCR offers an approach that is fast and inexpensive, and here enabled the identification of phylogroups. The phylogenetic distribution of E. coli subgroups isolated from water samples revealed higher frequencies of subgroups A1 and B23 in rivers impacted by human pollution sources, while subgroups D1 and D2 were associated with pristine sites, and subgroup B1 with domesticated animal sources, suggesting their use as a first screening for pollution source identification. A simple classification is also proposed based on phylogenetic subgroup distribution using the w-clique metric, enabling differentiation of polluted and unpolluted sites.

National Institutes Of Health Consensus Development Project On Criteria For Clinical Trials In Chronic Graft-versus-host Disease: I. The 2014 Diagnosis And Staging Working Group Report.

The 2005 National Institutes of Health (NIH) Consensus Conference proposed new criteria for diagnosing and scoring the severity of chronic graft-versus-host disease (GVHD). The 2014 NIH consensus maintains the framework of the prior consensus with further refinement based on new evidence. Revisions have been made to address areas of controversy or confusion, such as the overlap chronic GVHD subcategory and the distinction between active disease and past tissue damage. Diagnostic criteria for involvement of mouth, eyes, genitalia, and lungs have been revised. Categories of chronic GVHD should be defined in ways that indicate prognosis, guide treatment, and define eligibility for clinical trials. Revisions have been made to focus attention on the causes of organ-specific abnormalities. Attribution of organ-specific abnormalities to chronic GVHD has been addressed. This paradigm shift provides greater specificity and more accurately measures the global burden of disease attributed to GVHD, and it will facilitate biomarker association studies.

[consensus Of The Brazilian Society Of Rheumatology For The Diagnosis, Management And Treatment Of Lupus Nephritis].

To develop recommendations for the diagnosis, management and treatment of lupus nephritis in Brazil. Extensive literature review with a selection of papers based on the strength of scientific evidence and opinion of the Commission on Systemic Lupus Erythematosus members, Brazilian Society of Rheumatology. 1) Renal biopsy should be performed whenever possible and if this procedure is indicated; and, when the procedure is not possible, the treatment should be guided with the inference of histologic class. 2) Ideally, measures and precautions should be implemented before starting treatment, with emphasis on attention to the risk of infection. 3) Risks and benefits of treatment should be shared with the patient and his/her family. 4) The use of hydroxychloroquine (preferably) or chloroquine diphosphate is recommended for all patients (unless contraindicated) during induction and maintenance phases. 5) The evaluation of the effectiveness of treatment should be made with objective criteria of response (complete remission/partial remission/refractoriness). 6) ACE inhibitors and/or ARBs are recommended as antiproteinuric agents for all patients (unless contraindicated). 7) The identification of clinical and/or laboratory signs suggestive of proliferative or membranous glomerulonephritis should indicate an immediate implementation of specific therapy, including steroids and an immunosuppressive agent, even though histological confirmation is not possible. 8) Immunosuppressives must be used during at least 36 months, but these medications can be kept for longer periods. Its discontinuation should only be done when the patient achieve and maintain a sustained and complete remission. 9) Lupus nephritis should be considered as refractory when a full or partial remission is not achieved after 12 months of an appropriate treatment, when a new renal biopsy should be considered to assist in identifying the cause of refractoriness and in the therapeutic decision.

Thyroid nodules and differentiated thyroid cancer: update on the Brazilian consensus

Thyroid nodules are frequent findings, especially when sensitive imaging methods are used. Although thyroid cancer is relatively rare, its incidence is increasing, particularly in terms of small tumors, which have an uncertain clinical relevance. Most patients with differentiated thyroid cancer exhibit satisfactory clinical outcomes when treatment is appropriate, and their mortality rate is similar to that of the overall population. However, relapse occurs in a considerable fraction of these patients, and some patients stop responding to conventional treatment and eventually die from their disease. Therefore, the challenge is how to identify the individuals who require more aggressive disease management while sparing the majority of patients from unnecessary treatments and procedures. We have updated the Brazilian Consensus that was published in 2007, emphasizing the diagnostic and therapeutic advances that the participants, representing several Brazilian university centers, consider most relevant in clinical practice. The formulation of the present guidelines was based on the participants' experience and a review of the relevant literature.