PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Clustering Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46Linked to original sources
PubMedDate not supplied

A "cluster" based search scheme in peer-to-peer network.

This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved.

Algorithms↗
PubMedDate not supplied

Identification of groupings of graph theoretical molecular descriptors using a hybrid cluster analysis approach.

There is an abundance of structural molecular descriptors of various forms that have been proposed and tested over the years. Very often different descriptors represent, more or less, the same aspects of molecular structures and, thus, they have diminished discriminating power for the identification of different structural features that might contribute to the molecular property, or activity of interest. Therefore, it is essential that noncorrelated descriptors be employed to ensure the wider and the less inflated possible coverage of the chemical space. The most usual approach for reducing the number of descriptors and employing noncorrelated (or orthogonal) descriptors involves principal component analysis (PCA) or other factor analytical techniques. In this work we present an approach for determining relationships (groupings) among 240 graph-theoretical descriptors, as a means for selecting nonredundant ones, based on the application of cluster analysis (CA). To remove inherent biases and particularities of different CA algorithms, several clustering solutions, using these algorithms, were "hybridized" to obtain a reliable and confident overall solution concerning how the interrelationships within the data are structured. The calculated correlation coefficients between descriptors were used as a reference for a discussion on the different CA methods employed, and the resulted clusters of descriptors were statistically analyzed for deriving the intercorrelations between the different operators, weighting schemes and matrices used for the computation of these descriptors.

Cluster Analysis↗
PubMed1993

Cluster analysis of flow cytometric list mode data on a personal computer.

A cluster analysis algorithm, dedicated to analysis of flow cytometric data is described. The algorithm is written in Pascal and implemented on an MS-DOS personal computer. It uses k-means, initialized with a large number of seed points, followed by a modified nearest neighbor technique to reduce the large number of subclusters. Thus we combine the advantage of the k-means (speed) with that of the nearest neighbor technique (accuracy). In order to achieve a rapid analysis, no complex data transformations such as principal components analysis were used. Results of the cluster analysis on both real and artificial flow cytometric data are presented and discussed. The results show that it is possible to get very good cluster analysis partitions, which compare favorably with manually gated analysis in both time and in reliability, using a personal computer.

Algorithms↗
PubMed2006

Expressed sequence tag (EST) analysis of the erythrocytic stages of Babesia bovis.

Expressed sequence tags (ESTs) provide an efficient way to identify large numbers of genes expressed in a specific stage of the life cycle of an organism. Here we analysed approximately 13,000 ESTs derived from the erythrocytic stage of the apicomplexan parasite Babesia bovis. The ESTs were clustered in order to obtain information on the expression level of a gene and to increase sequence length and reliability. A total of 3522 clusters were obtained and annotated using BLAST algorithms. The clusters were estimated to represent approximately 2600 genes of which in total approximately 2.1 Mbp sequence information was obtained. Expression levels of the genes, as determined by the numbers of ESTs contained within a cluster, were compared to those of their closest homologs in the erythrocytic stage of Plasmodium falciparum and Toxoplasma gondii tachyzoites. Pathways that are represented relatively abundant in B. bovis are, amongst others, the purine salvage pathway (displaying characteristics not identified before in apicomplexans), isoprenoid biosynthesis in the apicoplast and many genes encoding mitochondrial proteins. Especially remarkable in the latter group are the F-type ATPases - which are hardly expressed in P. falciparum and T. gondii - and two highly expressed glycerol-3-phosphate dehydrogenases creating a shuttle possibly controlling the cytoplasmic NADH/NAD+ -ratio. A comparison of known antigenic proteins from Australian and American strains of B. bovis with the Israel strain used here identifies considerable sequence variation in the rhoptry associated protein-1 (RAP-1), merozoite surface proteins of the variable merozoite surface antigen (VMSA) family and spherical body proteins. Analysis of the EST clusters representing the variable erythocyte surface antigen family reveals many variant transcripts of which a few are dominant. Two putative pseudogenes also seem to be transcribed at high levels.

Animals↗
PubMed2000

Contig selection in physical mapping.

In physical mapping, one orders a set of genetic landmarks or a library of cloned fragments of DNA according to their position in the genome. Our approach to physical mapping divides the problem into smaller and easier subproblems by partitioning the probe set into independent parts (probe contigs). For this purpose we introduce a new distance function between probes, the averaged rank distance (ARD) derived from bootstrap resampling of the raw data. The ARD measures the pairwise distances of probes within a contig and smoothes the distances of probes across different contigs. It shows distinct jumps at contig borders. This makes it appropriate for contig selection by clustering. We have designed a physical mapping algorithm that makes use of these observations and seems to be particularly well suited to the delineation of reliable contigs. We evaluated our method on data sets from two physical mapping projects. On data from the recently sequenced bacterium Xylella fastidiosa, the probe contig set produced by the new method was evaluated using the probe order derived from the sequence information. Our approach yielded a basically correct contig set. On this data we also compared our method to an approach which uses the number of supporting clones to determine contigs. Our map is much more accurate. In comparison to a physical map of Pasteurella haemolytica that was computed using simulated annealing, the newly computed map is considerably cleaner. The results of our method have already proven helpful for the design of experiments aimed at further improving the quality of a map.

Algorithms↗
PubMed1994

SIMPLE34: an improved and enhanced implementation for VAX and Sun computers of the SIMPLE algorithm for analysis of clustered repetitive motifs in nucleotide sequences.

SIMPLE34 is an improved and enhanced version of SIMPLE for Vax and SunOS systems. It now provides a length-independent measure of the overall level of tri- and tetranucleotide motif clustering within nucleotide sequences and its significant deviation from random expectation. It now also provides information on tri- and tetranucleotide motifs showing higher levels of clustering than would be expected in random sequences. Sequence simplicity of test sequences can be judged with respect to random sequences generated on the basis of base composition, positional base composition or doublet frequency. These options can be used to investigate factors resulting in sequence simplicity.

Algorithms↗
PubMed2002

A prediction-based resampling method for estimating the number of clusters in a dataset.

BACKGROUND: Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems, such as the classification of tumors. An important statistical problem associated with tumor classification is the identification of new tumor classes using gene-expression profiles. Two essential aspects of this clustering problem are: to estimate the number of clusters, if any, in a dataset; and to allocate tumor samples to these clusters, and assess the confidence of cluster assignments for individual samples. Here we address the first of these problems. RESULTS: We have developed a new prediction-based resampling method, Clest, to estimate the number of clusters in a dataset. The performance of the new and existing methods were compared using simulated data and gene-expression data from four recently published cancer microarray studies. Clest was generally found to be more accurate and robust than the six existing methods considered in the study. CONCLUSIONS: Focusing on prediction accuracy in conjunction with resampling produces accurate and robust estimates of the number of clusters.

Algorithms↗
PubMedDate not supplied

Clustering gene expression patterns.

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.

Algorithms↗
PubMed2006

A genetic algorithm using hyper-quadtrees for low-dimensional K-means clustering.

The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d-dimensional space and the number of desired clusters k, k-means seeks a set of k cluster centers so as to minimize the sum of the squared Euclidean distance between each point and its nearest cluster center. However, the algorithm is very sensitive to the initial selection of centers and is likely to converge to partitions that are significantly inferior to the global optimum. We present a genetic algorithm (GA) for evolving centers in the k-means algorithm that simultaneously identifies good partitions for a range of values around a specified k. The set of centers is represented using a hyper-quadtree constructed on the data. This representation is exploited in our GA to generate an initial population of good centers and to support a novel crossover operation that selectively passes good subsets of neighboring centers from parents to offspring by swapping subtrees. Experimental results indicate that our GA finds the global optimum for data sets with known optima and finds good solutions for large simulated data sets.

Algorithms↗
PubMed2006

Automatic selection of arterial input function using cluster analysis.

Quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast MRI requires determination of the arterial input function (AIF) representing the delivery of intravascular tracer to tissue. This is typically accomplished manually by inspection of concentration time curves (CTCs) in regions containing the ICA, VA, and MCA. This is, however, a time consuming and operator dependent procedure. We suggest a completely automatic procedure for establishing the AIF based on a cluster analysis algorithm. In 20 normal subjects CBF maps calculated in 2 slices by the automatic procedure were compared to maps obtained with AIFs selected individually by 7 experienced operators. The average manual to automatic CBF ratio was 1.03+/-0.15 in the lower slice and 1.05+/-0.12 in the upper slice, demonstrating excellent agreement between the manual and automatic method. The algorithm provides means for objectively assessing AIF candidates in local AIF search algorithms designed to reduce bias due to delay and dispersion. Given the reproducibility and speed (10 s) of the automatic method, we speculate that it will greatly improve the accuracy of perfusion images and facilitate their use in clinical diagnosis and decision-making, particularly in acute stroke but also in cerebrovascular disease in general.

Aged↗
PubMed2026

Clustering patterns of behavioral and metabolic risk factors for noncommunicable diseases in Iran: findings from a national STEPS survey.

BACKGROUND: Noncommunicable diseases (NCDs) are the leading cause of mortality in Iran, driven by behavioral and metabolic risk factors that frequently co-occur. OBJECTIVE: To identify patterns of co-occurring behavioral and metabolic NCD risk factors among Iranian adults and characterize their demographic and socioeconomic correlates. METHODS: This cross-sectional study analyzed data from 16,618 adults aged ≥25 years who participated in Iran's 2021 nationally representative STEPS survey. Thirteen behavioral and metabolic variables, including physical activity, nutrition score, smoking frequency, alcohol intake, salt intake, body mass index, blood pressure, fasting plasma glucose, and lipid markers, were entered into a K-means clustering analysis. Clusters were characterized by their risk profiles and demographic/socioeconomic attributes. Multinomial logistic regression examined associations between cluster membership and sociodemographic factors. RESULTS: Five distinct behavioral-metabolic clusters emerged. The smokers-drinkers (SD) cluster (3.1%) comprised mostly older, less-educated men with high smoking and alcohol use. The healthy-low-risk (HLR) cluster (40.3%) showed favorable profiles and included younger, more educated individuals. The physically active (PA) cluster (6.6%) was characterized mainly by younger men with markedly high physical activity levels. The dyslipidemic (DLP) cluster (26.0%) exhibited high dyslipidemia and overweight prevalence, while the hypertensive-diabetic (HTD) cluster (24.0%) had the highest obesity, hypertension, and diabetes rates, common among older urban adults. CONCLUSION: Behavioral and metabolic NCD risk factors in Iran formed five distinct co-occurrence patterns. Nearly half of adults belonged to metabolically high-risk clusters, highlighting the need for targeted prevention strategies that combine lifestyle interventions with screening and management of obesity, hypertension, diabetes, and dyslipidemia.

Humans↗
PubMed1982

Numerical and chemical classification of Nocardia amarae.

Twenty-one strains of Nocardia amarae and marker cultures of Mycobacterium, Nocardia, Rhodococcus and the 'aurantiaca' taxon were subjected to numerical phenetic analyses using 92 unit characters. The data were examined using the simple matching (SSM), Jaccard (SJ) and pattern (DP) coefficients and clustering was achieved using the unweighted average linkage algorithm. Neither cluster nor aggregate cluster composition was markedly affected by the coefficient used or by test error, estimated at 1.5%. The N. amarae strains formed a distinct and homogeneous cluster which showed its highest similarity to phena equated with Nocardia asteroides, Nocardia brasiliensis and Nocardia otitidis-caviarum. The non-hydroxylated fatty acid composition and overall size of the mycolic acids was similar to that found to be characteristic of Nocardia sensu stricto, though the long-chain in the 2-position of the mycolic acids was relatively much richer in monounsaturated components. Nocardia amarae, in containing dihydrogenated menaquinones with nine isoprene units, is clearly distinguished from established representatives of Nocardia.

Fatty Acids↗
PubMed2006

A multiscale expectation-maximization semisupervised classifier suitable for badly posed image classification.

This paper deals with the problem of badly posed image classification. Although underestimated in practice, bad-posedness is likely to affect many real-world image classification tasks, where reference samples are difficult to collect (e.g., in remote sensing (RS) image mapping) and/or spatial autocorrelation is relevant. In an image classification context affected by a lack of reference samples, an original inductive learning multiscale image classifier, termed multiscale semisupervised expectation maximization (MSEM), is proposed. The rationale behind MSEM is to combine useful complementary properties of two alternative data mapping procedures recently published outside of image processing literature, namely, the multiscale modified Pappas adaptive clustering (MPAC) algorithm and the sample-based semisupervised expectation maximization (SEM) classifier. To demonstrate its potential utility, MSEM is compared against nonstandard classifiers, such as MPAC, SEM and the single-scale contextual SEM (CSEM) classifier, besides against well-known standard classifiers in two RS image classification problems featuring few reference samples and modestly useful texture information. These experiments yield weak (subjective) but numerous quantitative map quality indexes that are consistent with both theoretical considerations and qualitative evaluations by expert photointerpreters. According to these quantitative results, MSEM is competitive in terms of overall image mapping performance at the cost of a computational overhead three to six times superior to that of its most interesting rival, SEM. More in general, our experiments confirm that, even if they rely on heavy class-conditional normal distribution assumptions that may not be true in many real-world problems (e.g., in highly textured images), semisupervised classifiers based on the iterative expectation maximization Gaussian mixture model solution can be very powerful in practice when: 1) there is a lack of reference samples with respect to the problem/model complexity and 2) texture information is considered negligible (i.e., a piecewise constant image model holds).

Algorithms↗
PubMed1999

An EM-type Algorithm for Ordered Restriction Map Alignment.

Constructing restriction maps is one of the important steps towards the determination of DNA sequences. Recently, the single-molecule approaches to constructing restriction maps, such as Optical Mapping by D. Schwartz et al., have developed. In practice, with the single-molecule approach like Optical Mapping, the identification of the restriction sites is complicated by several error factors due to resolving power of biological experiments. The ordered restriction map alignment problem is a problem to estimate the actual restriction sites from many imprecise copies of map from single molecule. In this paper, we formulate the problem on the basis of the statistical maximum likelihood estimate, and propose a new efficient local search algorithm for this problem, by applying the Expectation-Maximization (EM) algorithm along with the concept of two-clustering. Our algorithm works well for a lot of sets of simulated data, some of which we believe more difficult than the actual cases.

Journal Article↗
PubMedDate not supplied

Clever and efficient method for searching optimal geometries of lennard-jones clusters.

An unbiased algorithm for determining global minima of Lennard-Jones (LJ) clusters is proposed in the present study. In the algorithm, a global minimum is searched by using two operators: one modifies a cluster configuration by moving atoms to the most stable positions on the surface of a cluster and the other gives a perturbation on a cluster configuration by moving atoms near the center of mass of a cluster. The moved atoms are selected by employing contribution of the atoms to the potential energy of a cluster. It was possible to find new global minima for LJ506, LJ521, LJ536, LJ537, LJ538, and LJ541 together with putative global minima of LJ clusters of 10-561 atoms reported in the literature. This indicates that the present method is clever and efficient for cluster geometry optimization.

Journal Article↗
PubMed2003

An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings.

Multi-electrode recordings in neural tissue contain the action potential waveforms of many closely spaced neurons. While we can observe the action potential waveforms, we cannot observe which neuron is the source for which waveform nor how many source neurons are being recorded. Current spike-sorting algorithms solve this problem by assuming a fixed number of source neurons and assigning the action potentials given this fixed number. We model the spike waveforms as an anisotropic Gaussian mixture model and present, as an alternative, a reversible-jump Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the number of source neurons and to assign each action potential to a source. We derive this MCMC algorithm and illustrate its application using simulated three-dimensional data and real four-dimensional feature vectors extracted from tetrode recordings of rat entorhinal cortex neurons. In the analysis of the simulated data our algorithm finds the correct number of mixture components (sources) and classifies the action potential waveforms with minimal error. In the analysis of real data, our algorithm identifies clusters closely resembling those previously identified by a user-dependent graphical clustering procedure. Our findings suggest that a reversible-jump MCMC algorithm could offer a new strategy for designing automated spike-sorting algorithms.

Action Potentials↗
PubMed2004

Genome and species relationships in genus Avena based on RAPD and AFLP molecular markers.

Species and genome relationships among 11 diploid (A and C genomes), five tetraploid (AB and AC genomes) and two hexaploid (ACD genome) Avena taxa were investigated using amplified fragment length polymorphisms (AFLPs) and random amplified polymorphic DNA (RAPD) markers. The two primer pairs used for the AFLP reactions produced a total of 354 polymorphic bands, while 187 reproducible bands were generated using ten RAPD primers. Genetic similarities amongst the entries were estimated using the Jaccard and Dice algorithms, and cluster analyses were performed using UPGMA and neighbor joining methods. Principle coordinate analysis was also applied. The highest cophenetic correlation coefficient was obtained for the Jaccard algorithm and UPGMA clustering method ( r=0.99 for AFLP and r=0.94 for RAPD). No major clustering differences were present between phenograms produced with AFLPs and RAPDs. Furthermore, data produced with AFLPs and RAPDs were highly correlated ( r=0.92), indicating the reliability of our results. All A genome diploid taxa are clustered together according to their karyotype. The AB genome tetraploids were found to form a subcluster within the A(s )genome diploids (AFLPs), indicating their near-autoploid origin. The AC genome tetraploids are clustered to the ACD genome hexaploids. Finally, the C genome diploids form an outer branch, indicating the major genomic divergence between the A and C genomes in Avena.

Cluster Analysis↗