160 resultados para Evolutionary clustering


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Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

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The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

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A fundamental issue in biology is explaining the diversity of coloration found in nature. Birds provide some of the best-studied examples of the evolution and causes of color variation and some of the most arresting color displays in the natural world. They possess perhaps the most richly endowed visual system of any vertebrate, including UV-A sensitivity and tetrachromatic color vision over the 300-700-nm waveband. Birds provide model systems for the multidisciplinary study of animal coloration and color vision. Recent advances in understanding avian coloration and color vision are due to recognition that birds see colors in a different way than humans do and to the ready availability of small spectrometers. We summarize the state of the current field, recent trends, and likely future directions.

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Polymerase chain reaction (PCR) sequencing of specific viral gene segments was used to investigate the phylogenetic relationships among the orbiviruses. Sequence comparisons of the bluetongue virus (BTV) RNA3 from different regions of the world (North America, South Africa, India, Indonesian, Malaysia, Australia and the Caribbean region) showed that geographic separation had resulted in significant divergence, consistent with the evolution of distinct viral populations. There were at least 3 topotypes (Gould, 1987); the Australasian, African - American and another topotype represented by BTV 15 isolated in Australia in 1986. The topotypes of BTV had RNA3 nucleotide sequences that differed by approximately 20 per cent. Analysis of BTV-specific gene segments from animal and insect specimens showed that bluetongue viruses had entered northern Australia from South East Asia, possibly by wind-borne vectors. Nucleotide sequence comparisons were used to show the close genetic relationship between BTV 2 (Ona-A strain) from Florida and BTV 12 from Jamaica, and to investigate the reassortment of BTV genome segments in nature. The mutation rates of the BTV RNA2 and RNA3 segments were estimated to be of the order of 10(-4) nucleotide changes/site/year, similar in magnitude to that reported for other RNA viruses.

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The theory of ‘punctuated equilibria’ was formulated by two paleontologists, Niles Eldredge and Stephen Gould, in 1972 and has been the focus of considerable controversy within the evolutionary sciences ever since. The primary intentions of this thesis are to relate the history of punctuated equilibria and to examine how it has affected evolutionary science. Several modes of analysis have been used to illuminate the history: The sociological perspectives of Pierre Bourdieu and Bruno Latour (who see scientific debate as a ‘struggle for authority’); Rhetorical analysis of some of the key documents; Communication with practising scientists via questionnaires and correspondence; Citation Analysis. Chapter 1 gives a short summary of the history and introduces the methods and socio-philosophical perspectives used to illuminate the history. Chapters 3 and 4 examine the rhetorical process by which Eldredge and Gould constructed the ‘punctuationist revolution’. Chapters 2, 3, 5 and 6 relate the history of punctuated equilibria.

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A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min–min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach.

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This thesis examined genetic variation among populations of galaxiid fish of different life histories. Evidence of incipient speciation was found among several landlocked populations; however, little genetic differentiation was found among Australian coastal populations. Evidence was found to suggest a marine ancestry for galaxiids and several taxonomic revisions were suggested.

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A long-standing question in the field of immunology concerns the factors that contribute to Th cell epitope immunodominance. For a number of viral membrane proteins, Th cell epitopes are localized to exposed protein surfaces, often overlapping with Ab binding sites. It has therefore been proposed that Abs on B cell surfaces selectively bind and protect exposed protein fragments during Ag processing, and that this interaction helps to shape the Th cell repertoire. While attractive in concept, this hypothesis has not been thoroughly tested. To test this hypothesis, we have compared Th cell peptide immunodominance in normal C57BL/6 mice with that in C57BL/6MT/MT mice (lacking normal B cell activity). Animals were first vaccinated with DNA constructs expressing one of three different HIV envelope proteins, after which the CD4 T cell response profiles were characterized toward overlapping peptides using an IFN- ELISPOT assay. We found a striking similarity between the peptide response profiles in the two mouse strains. Profiles also matched those of previous experiments in which different envelope vaccination regimens were used. Our results clearly demonstrate that normal Ab activity is not required for the establishment or maintenance of Th peptide immunodominance in the HIV envelope response. To explain the clustering of Th cell epitopes, we propose that localization of peptide on exposed envelope surfaces facilitates proteolytic activity and preferential peptide shuttling through the Ag processing pathway.

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Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt with properly. We propose SWEM algorithm that exploits the Expectation Maximization technique to address these challenges. SWEM is not only able to process the stream in an incremental manner, but also capable to adapt to changes happened in the underlying stream distribution.

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Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.

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This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.

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Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.

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An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) Az of 0.9786 has been achieved.

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Model systems of sodium iodide dissolved in dimethyl ether or 1,2-dimethoxyethane (glyme) were studied in order to investigate the structural and dynamic properties of ionic solutions in small and polymeric ethers. Full molecular dynamics simulations were performed at a range of different salt concentrations. An algorithm was designed which assigns ions to clusters and then calculates all the terms which contribute to ionic conductivity. In dilute solutions, free ions are the most common ionic species, followed by ion pairs. As the concentration increases, pairs become the most common species, with significant concentrations of clusters with 3 through 6 ions. Changing the solvent from dimethyl ether to glyme significantly decreases the ion clustering due to the chelate effect in which the two oxygens on a solvent stabilize an associated cation. The conductivity in stable systems is shown to be primarily the result of the movement of free ions and the relative movement of ions within neutral pairs. The Nernst-Einstein relation, commonly used in the discussion of polymer electrolytes, is shown to be inadequate to quantitatively describe conductivity in the model systems.