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 1,747 records · Page 97Linked to original sources

PubMed2005

A computerized cellular imaging system for high content analysis in Monastrol suppressor screens.

In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS-MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS-MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS-MMA with visual analysis, as well as clustering of the drug compounds under evaluation.

Algorithms↗

PubMed2005

Aggregation-fragmentation in a model of DNA-mediated colloidal assembly.

We present results from an off-lattice Monte Carlo simulation of DNA-mediated colloidal assembly. In this simulation, the aggregation-fragmentation of a binary mixture of DNA-coated colloidal particles is studied through a simplified model of base-pair hybridization. Bonding between monomers is modeled as a simple temperature-sensitive A/B-type interaction, where type A and B monomers can bond to only the opposite type (no A/A or B/B attachments are allowed). The actual chemistry of base-pair hybridization is not included in the model. The morphological structures of the clusters formed as well as the kinetics of growth are analyzed in our 2D simulations. The fractal dimension and kinetic growth exponents for clusters formed near the DNA "melting" temperature agree with those seen previously for 2D diffusion-limited cluster aggregation (DLCA) models. The clusters appear more compact, exhibiting signs of local order at intermediate temperature values. At higher temperatures, the formation of large clusters is not favorable under the action of temperature-dependent fragmentation, and the system eventually reaches a steady state as a collection of small aggregates. The temperature profile for this dissolution of the colloidal assembly is sharp, indicating that the selective hybridization process provides a highly sensitive measurement tool. At high temperatures, we analyze the steady-state behavior of the average cluster size in terms of an aggregation-fragmentation model.

Algorithms↗

PubMed2006

Bayesian model averaging of naive Bayes for clustering.

This paper considers a Bayesian model-averaging (MA) approach to learn an unsupervised naive Bayes classification model. By using the expectation model-averaging (EMA) algorithm, which is proposed in this paper, a unique naive Bayes model that approximates an MA over selective naive Bayes structures is obtained. This algorithm allows to obtain the parameters for the approximate MA clustering model in the same time complexity needed to learn the maximum-likelihood model with the expectation-maximization algorithm. On the other hand, the proposed method can also be regarded as an approach to an unsupervised feature subset selection due to the fact that the model obtained by the EMA algorithm incorporates information on how dependent every predictive variable is on the cluster variable.

Algorithms↗

PubMed1999

[Neural cluster structure with single component prediction in multiple variable systems for X-ray fluorescence spectrometry].

A neural cluster structure with single component prediction (NCSCP) was proposed for X-ray fluorescence spectrometry in a multivariable system. The neural cluster structure is built by the collection of a group of neurons which have close relationships among one another. In X-ray fluorescence analysis, the structure is constructed by choosing the elements in which there exist serious matrix effects, and deleting the components containing large noise. The predictability of the neural cluster structure was compared with that of the classical backward error propagation algorithm with single component prediction. The results show that the neural cluster structure is significantly superior to the classical algorithm in prediction accuracy, antidisturbance and the predictabilty to outliers.

English Abstract↗

PubMed2005

Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis.

MOTIVATION: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. RESULTS: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. AVAILABILITY: Neuroshell 2 is commercially available from Ward Systems.

Algorithms↗

PubMed2001

Fuzzy clustering of gait patterns of patients after ankle arthrodesis based on kinematic parameters.

Kinematic parameters for 10 normal subjects and 10 patients with ankle arthrodesis are grouped using the fuzzy cluster paradigm. The features chosen for clustering are Euler angles of the sagittal plane in the hindfoot, the forefoot and combined hindfoot and forefoot joints. Gait patterns are identified using information provided by cluster validity techniques, giving three, three and two clusters for the hindfoot, forefoot and combined hindfoot and forefoot joints, respectively. The cluster centers represent distinct walking strategies adopted by normal subjects and patients after ankle arthrodesis. Utilizing angle values normalized by gait cycle, it is possible to classify any subject and to generate an individual's membership value for each of the clusters. The clinical utility of the fuzzy clustering approach is demonstrated with data for subjects with ankle arthrodesis, where changes in membership of the clusters provide an objective technique for measuring changes of gait pattern after ankle arthrodesis. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided in this study.

Adult↗

PubMed2003

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks.

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Algorithms↗

PubMed1990

The use of cluster analysis in clinical chemical diagnosis of liver diseases.

Diagnostic judgement is usually based on recognition of patterns. Unfortunately more than three quantitative data cannot be judged simultaneously without help of mathematical methods. Working on laboratory reports, a clinician usually goes linearly through the columns and reduces quantitative to qualitative data. Therefore the medical decision process should be improved if data reduction is performed with the aid of mathematical methods for pattern recognition. A total of 191 consecutive outpatients with a tentative or proven diagnosis of hepatobiliary disease were examined clinically, clinically chemically and partly histologically. Nineteen clinical chemical parameters were determined. Prior to pattern cognition, a principal component analysis was performed. Using six factors, accounting for 72.4% of total variance, cluster analysis was done, applying a hierarchical algorithm for ascertaining a starting partition, followed by the k-means algorithm. The validity of the solution was scrutinized, and a stable structure was found with nine clusters. Patients with a rejected suspect of liver disease were mainly located in clusters 1, 6 and 7. Cluster 1 also contains patients with compensated cirrhosis without inflammation, idiopathic hyperbilirubinaemia, focal nodular hyperplasia and haemangioma of the liver. In contrast, one third of cirrhoses, all with inflammatory activity were assigned to cluster 5. Patients with primary biliary disease were distributed among clusters 2, 3 and 4. All malignant neoplasias were assigned to cluster 9. More than 50% of fatty livers were classified to cluster 7. Cluster 2 and 8 contain only one patient with primary biliary cirrhosis (cluster 2) and fatty liver hepatitis (cluster 8). The follow-up of 66 patients also showed clinically meaningful changes of cluster assignment.

Cluster Analysis↗

PubMed2005

The use of edge-betweenness clustering to investigate biological function in protein interaction networks.

BACKGROUND: This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters. RESULTS: Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions. CONCLUSION: This method provides a useful tool for rapid screening of small to medium size protein interaction datasets.

Algorithms↗

PubMed2004

Sensitivity of finite helical axis parameters to temporally varying realistic motion utilizing an idealized knee model.

Various uses of the screw or helical axis have previously been reported in the literature in an attempt to quantify the complex displacements and coupled rotations of in vivo human knee kinematics. Multiple methods have been used by previous authors to calculate the axis parameters, and it has been theorized that the mathematical stability and accuracy of the finite helical axis (FHA) is highly dependent on experimental variability and rotation increment spacing between axis calculations. Previous research has not addressed the sensitivity of the FHA for true in vivo data collection, as required for gait laboratory analysis. This research presents a controlled series of experiments simulating continuous data collection as utilized in gait analysis to investigate the sensitivity of the three-dimensional finite screw axis parameters of rotation, displacement, orientation and location with regard to time step increment spacing, utilizing two different methods for spatial location. Six-degree-of-freedom motion parameters are measured for an idealized rigid body knee model that is constrained to a planar motion profile for the purposes of error analysis. The kinematic data are collected using a multicamera optoelectronic system combined with an error minimization algorithm known as the point cluster method. Rotation about the screw axis is seen to be repeatable, accurate and time step increment insensitive. Displacement along the axis is highly dependent on time step increment sizing, with smaller rotation angles between calculations producing more accuracy. Orientation of the axis in space is accurate with only a slight filtering effect noticed during motion reversal. Locating the screw axis by a projected point onto the screw axis from the mid-point of the finite displacement is found to be less sensitive to motion reversal than finding the intersection of the axis with a reference plane. A filtering effect of the spatial location parameters was noted for larger time step increments during periods of little or no rotation.

Algorithms↗

PubMed2001

Spoligotype diversity of Mycobacterium bovis strains isolated in France from 1979 to 2000.

The molecular fingerprints of 1,349 isolates of Mycobacterium bovis received between 1979 and August 2000 at Agence Française de Sécurité Sanitaire des Aliments (Afssa) have been obtained by spoligotyping. The majority of the isolates (1,266) were obtained from cattle living in France. An apparently high level of heterogeneity was observed between isolates. One hundred sixty-one spoligotypes were observed in total, of which 153 were from French isolates. The two predominant spoligotypes, designated BCG-like and GB54, accounted for 26 and 12% of the isolates, respectively. In addition, 84% of the spoligotypes were found fewer than 10 times. Analysis of the results by clustering and parsimony-based algorithms revealed that the majority of the spoligotypes were closely related. The predominant spoligotype was identical to that of the vaccine strain Mycobacterium bovis BCG, which was isolated in France at the end of the 19th century. Some spoligotypes were closely associated with restricted geographical areas. Interestingly, some spoligotypes, which were frequently observed in France, were also observed in neighboring countries. Conversely, few spoligotypes were common to France and England, and those that were shared were observed at very different frequencies. This last point illustrates the potential role for an international data bank, which could help trace the spread of M. bovis across national borders.

Animals↗

PubMed1992

The rapid generation of mutation data matrices from protein sequences.

An efficient means for generating mutation data matrices from large numbers of protein sequences is presented here. By means of an approximate peptide-based sequence comparison algorithm, the set sequences are clustered at the 85% identity level. The closest relating pairs of sequences are aligned, and observed amino acid exchanges tallied in a matrix. The raw mutation frequency matrix is processed in a similar way to that described by Dayhoff et al. (1978), and so the resulting matrices may be easily used in current sequence analysis applications, in place of the standard mutation data matrices, which have not been updated for 13 years. The method is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fast enough to generate a matrix from a specific family or class of proteins in minutes. Differences observed between our 250 PAM mutation data matrix and the matrix calculated by Dayhoff et al. are briefly discussed.

Algorithms↗

PubMed2006

BicAT: a biclustering analysis toolbox.

SUMMARY: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments. AVAILABILITY: The BicAT toolbox is freely available at http://www.tik.ee.ethz.ch/sop/bicat and runs on all operating systems. The Java source code of the program and a developer's guide is provided on the website as well. Therefore, users may modify the program and add further algorithms or extensions.

Algorithms↗

PubMed2005

Refined phylogenetic profiles method for predicting protein-protein interactions.

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method. RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.

Algorithms↗

PubMed2004

Isolation and characterization of mesophilic, oxalate-degrading Streptomyces from plant rhizosphere and forest soils.

The present work was aimed at the isolation of additional new pure cultures of oxalate-degrading Streptomyces and its preliminary characterization for further work in the field of oxalate metabolism and taxonomic studies. Mesophilic, oxalate-degrading Streptomyces were enriched and isolated from plant rhizosphere and forest soil samples. Strains were examined for cultural, morphological (spore chain morphology, spore mass colour, diffusible and melanin pigment production), physiological (antibiosis, growth in the presence of inhibitory compounds, assimilation of organic acids and enzyme substrates) and chemotaxonomic characters (cellular lipid components and diagnostic cell-wall diamino acid). The taxonomic data obtained were analysed by using the simple matching (SSM) and Jaccard (Sj) coefficients, clustering was achieved using the UPGMA algorithm. All strains were able to utilize sodium-, potassium-, calcium- and ammonium-oxalate salts. Based on the results of numerical taxonomy, isolates were grouped into five cluster groups with a > or =70% S(SM) similarity level. Streptomyces rochei was the most common of the cluster groups, with a Willcox probability of P > 0.8. Streptomyces antibioticus, S. anulatus, S. fulvissimus, S. halstedii and S. violaceusniger are newly reported as oxalate-utilizing Streptomyces.

Ecosystem↗

PubMed2004

Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.

BACKGROUND: In patients with acute myeloid leukemia (AML), the presence or absence of recurrent cytogenetic aberrations is used to identify the appropriate therapy. However, the current classification system does not fully reflect the molecular heterogeneity of the disease, and treatment stratification is difficult, especially for patients with intermediate-risk AML with a normal karyotype. METHODS: We used complementary-DNA microarrays to determine the levels of gene expression in peripheral-blood samples or bone marrow samples from 116 adults with AML (including 45 with a normal karyotype). We used unsupervised hierarchical clustering analysis to identify molecular subgroups with distinct gene-expression signatures. Using a training set of samples from 59 patients, we applied a novel supervised learning algorithm to devise a gene-expression-based clinical-outcome predictor, which we then tested using an independent validation group comprising the 57 remaining patients. RESULTS: Unsupervised analysis identified new molecular subtypes of AML, including two prognostically relevant subgroups in AML with a normal karyotype. Using the supervised learning algorithm, we constructed an optimal 133-gene clinical-outcome predictor, which accurately predicted overall survival among patients in the independent validation group (P=0.006), including the subgroup of patients with AML with a normal karyotype (P=0.046). In multivariate analysis, the gene-expression predictor was a strong independent prognostic factor (odds ratio, 8.8; 95 percent confidence interval, 2.6 to 29.3; P<0.001). CONCLUSIONS: The use of gene-expression profiling improves the molecular classification of adult AML.

Acute Disease↗

PubMed2006

Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration.

Mutations of lamin A/C (LMNA) cause a wide range of human disorders, including progeria, lipodystrophy, neuropathies and autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD). EDMD is also caused by X-linked recessive loss-of-function mutations of emerin, another component of the inner nuclear lamina that directly interacts with LMNA. One model for disease pathogenesis of LMNA and emerin mutations is cell-specific perturbations of the mRNA transcriptome in terminally differentiated cells. To test this model, we studied 125 human muscle biopsies from 13 diagnostic groups (125 U133A, 125 U133B microarrays), including EDMD patients with LMNA and emerin mutations. A Visual and Statistical Data Analyzer (VISDA) algorithm was used to statistically model cluster hierarchy, resulting in a tree of phenotypic classifications. Validations of the diagnostic tree included permutations of U133A and U133B arrays, and use of two probe set algorithms (MAS5.0 and MBEI). This showed that the two nuclear envelope defects (EDMD LMNA, EDMD emerin) were highly related disorders and were also related to fascioscapulohumeral muscular dystrophy (FSHD). FSHD has recently been hypothesized to involve abnormal interactions of chromatin with the nuclear envelope. To identify disease-specific transcripts for EDMD, we applied a leave-one-out (LOO) cross-validation approach using LMNA patient muscle as a test data set, with reverse transcription-polymerase chain reaction (RT-PCR) validations in both LMNA and emerin patient muscle. A high proportion of top-ranked and validated transcripts were components of the same transcriptional regulatory pathway involving Rb1 and MyoD during muscle regeneration (CRI-1, CREBBP, Nap1L1, ECREBBP/p300), where each was specifically upregulated in EDMD. Using a muscle regeneration time series (27 time points) we develop a transcriptional model for downstream consequences of LMNA and emerin mutations. We propose that key interactions between the nuclear envelope and Rb and MyoD fail in EDMD at the point of myoblast exit from the cell cycle, leading to poorly coordinated phosphorylation and acetylation steps. Our data is consistent with mutations of nuclear lamina components leading to destabilization of the transcriptome in differentiated cells.

Biopsy↗

PubMed1993

Analytical problems in detecting rapid insulin secretory pulses in normal humans.

The present study was undertaken to define the optimal experimental and analytical conditions necessary to reproducibly detect, in the systemic blood, small-amplitude rapid oscillations (period 8-15 min) of insulin and C-peptide. Samples for insulin, C-peptide, and glucose were drawn at 2-min intervals for 2 h from six normal subjects during constant glucose infusion and from five of the same subjects under basal conditions. To reduce measurement error, insulin and C-peptide levels were measured 16 times at each time point. Three algorithms for pulse analysis (ULTRA, Cluster, PulseFit) were used to identify significant pulses, whereas autocorrelation and spectral analysis were used to identify potential regular periodic components in the data. In three of the five subjects studied under basal conditions, regular rapid oscillations could be consistently detected by autocorrelation when the analysis was based on eight replicates but not on duplicate series. In the remaining two basal studies and in all studies during glucose infusion, the majority of profiles did not have a significant periodic component. However, formal pulse analysis demonstrated that the number of pulses was similar during glucose infusion and basal conditions. Reproducibility was enhanced by increasing the number of replicates used in the analysis. We conclude that in the analysis of small-amplitude rapid insulin and C-peptide oscillations, the sensitivity and specificity of the analysis is likely to be enhanced by performing multiple estimations at each time point and by using a minimum of two contrasting analytical approaches for pulse detection, which incorporate a method evaluating periodicity in conjunction with a pulse detection program designed to evaluate each individual oscillation separately.

Adult↗