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Results for “cluster analysis”

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PubMed2005

Functional cluster analysis of CT perfusion maps: a new tool for diagnosis of acute stroke?

CT perfusion imaging constitutes an important contribution to the early diagnosis of acute stroke. Cerebral blood flow (CBF), cerebral blood volume (CBV) and time-to-peak (TTP) maps are used to estimate the severity of cerebral damage after acute ischemia. We introduce functional cluster analysis as a new tool to evaluate CT perfusion in order to identify normal brain, ischemic tissue and large vessels. CBF, CBV and TTP maps represent the basis for cluster analysis applying a partitioning (k-means) and density-based (density-based spatial clustering of applications with noise, DBSCAN) paradigm. In patients with transient ischemic attack and stroke, cluster analysis identified brain areas with distinct hemodynamic properties (gray and white matter) and segmented territorial ischemia. CBF, CBV and TTP values of each detected cluster were displayed. Our preliminary results indicate that functional cluster analysis of CT perfusion maps may become a helpful tool for the interpretation of perfusion maps and provide a rapid means for the segmentation of ischemic tissue.

Adult↗

PubMed2005

EM clustering analysis of diabetes patients basic diagnosis index.

Cluster analysis can group similar instances into same group and different instances into different groups. It assigns classes to samples without known the classes in advance. EM clustering algorithm can find number of distributions of generating data and build "mixture models". It identifies groups that are either overlapping or varying sizes and shapes. In this project, by using EM in Weka system, diabetes patient basic diagnosis index data have been analyzed for clustering.

Algorithms↗

PubMed2000

Cluster analysis of clinical seizure semiology of psychogenic nonepileptic seizures.

PURPOSE: To develop an objective classification of psychogenic nonepileptic seizures (NES) based on cluster analysis of clinical seizure semiology. METHODS: We studied the clinical seizure semiology in 27 patients with psychogenic NES documented by prolonged video-EEG monitoring. We analyzed the following clinical symptoms: clonic and hypermotor movements as well as trembling of the upper and/or lower extremities, pelvic thrusting, head movements, tonic posturing backward of the head, and falling. We used cluster analysis to identify symptoms occurring together in a systematic way and thus tried to achieve a clinical classification of psychogenic NES. RESULTS: We could identify three symptom clusters. Cluster 1 was characterized by clonic and hypermotor movements of the extremities, pelvic thrusting, head movements, and tonic posturing of the head, and therefore was named "psychogenic motor seizures." Cluster 2 comprised trembling of the upper and lower extremities and was termed "psychogenic minor motor or trembling seizures." Cluster 3 consisted of falling to the floor as the only symptom and was referred to as "psychogenic atonic seizures." CONCLUSIONS: Our study represents the first study to analyze the clinical semiology of psychogenic NES by cluster analysis, which should be useful for an objective classification of psychogenic NES. This classification should allow both a better characterization of psychogenic NES and an easier differential diagnosis against specific epileptic seizures.

Adult↗

PubMed2002

Using hierarchical cluster analysis in nursing research.

This article is a pedagogical piece on hierarchical cluster analysis, a method for investigating the structure underlying data. Such methods are useful for finding similar groups of cases in data sets when it is not known a priori how many groups are present. The article is laid out asfollows: First, a brief history and overview of the methods is presented; second, an illustrative example with a small hypothetical data set is used to clarify fundamental concepts; third, hierarchical cluster analysis is applied to a data set from the author's own program of research to illustrate one way in which the methods may be employed in nursing research; fourth, the limitations of the methods are discussed; and finally, a list of suggested readings, at varying levels of detail. are provided for the interested researcher.

Cluster Analysis↗

PubMed2005

Cluster analysis: an alternative method for covariate selection in population pharmacokinetic modeling.

To be analyzed, the heterogeneity characterizing biological data calls for using appropriate models involving numerous variables. A high variable number could become problematic when one needs to determine a priori the most significant variable combination in order to reduce the inter-individual variability (IIV). Alternatively to multiple introductions of single variables, we propose a single introduction of a multivariate variable. We present cluster analysis as a stratification strategy that combines the initial single covariates to build a multivariate categorical covariate. It is an exploratory multivariate analysis that outlines homogeneous categories of individuals (clusters) according to similarities from the set of covariates. It includes many clustering techniques combining a distance measure and a linkage algorithm, and leading to various stratification patterns. The cluster analysis approach is illustrated by a case study on cortisol kinetics in 82 patients after intravenous bolus administration of synacthen (synthetic corticotropin). Using NONMEM, a basic infusion model was initially achieved for cortisol, and then a classical covariate selection was applied to improve IIV. The best fit was between the elimination rate constant k and the body mass index (BMI), which improved IIV of k. An alternative method is presented consisting in the population into homogeneous and non-overlapping groups by applying a cluster analysis. Such categorization (or clustering) was carried out using Euclidean distance and complete-linkage algorithm. This algorithm gave five dissimilar clusters that differed by increasing BMI, obesity duration, and waist-hip ratio. The dispersion of k according to the five clusters showed three distinctvariation ranges a priori, which corresponded a posteriori(after NONMEM modeling) to three sub-populations of k. After grouping the clusters that had similar variation ranges of k, we obtained three final clusters representing non-obese, intermediate, and extreme obese sub-populations. The pharmacokinetic model based on three clusters was better than the basic model, similar to the classical covariate model, but had a stronger interpretability: It showed that the stimulation and elimination of cortisol were higher in the extreme obese followed by intermediate then non-obese subjects.

Adrenocorticotropic Hormone↗

PubMed1994

Protein secondary structure from circular dichroism spectroscopy. Combining variable selection principle and cluster analysis with neural network, ridge regression and self-consistent methods.

Different approaches to improve the analysis of protein secondary structure from circular dichroism spectra are compared. Grouping proteins based on the similarity of their circular dichroism spectra, using cluster analysis methods, was utilized as a new way of implementing variable selection. The performance of three basic methods (neural networks, ridge regression and singular value decomposition) was evaluated in combination with three approaches to improve the predictions; namely, variable selection, cluster analysis and the self-consistent method. Cluster analysis performed on the basis set proteins resulted in three clusters, subanalyses of which provide a new way of performing variable selection. The neural network with two hidden layers performed better than that with one hidden layer and was combined with variable selection. Inclusion of the variable selection principle improved the performance of all three basic methods. While the neural network method performed slightly better than the other two methods at the basic level, the inclusion of variable selection led to similar performance indices for all three methods.

Circular Dichroism↗

PubMed2006

AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets.

MOTIVATION: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing. RESULTS: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose. AVAILABILITY: AClAP is available at the "AClAP" section of the website http://www.scfarm.unibo.it.

Algorithms↗

PubMedDate not supplied

The identification of credit card encoders by hierarchical cluster analysis of the jitters of magnetic stripes.

The relative bit density variation graphs of 207 specimen credit cards processed by 12 encoding machines were examined first visually, and then classified by means of hierarchical cluster analysis. Twenty-nine credit cards being treated as 'questioned' samples were tested by way of cluster analysis against 'controls' derived from known encoders. It was found that hierarchical cluster analysis provided a high accuracy of identification with all 29 'questioned' samples classified correctly. On the other hand, although visual comparison of jitter graphs was less discriminating, it was nevertheless capable of giving a reasonably accurate result.

Cluster Analysis↗

PubMed1998

[Appllcation of PYGC and the hierarchical clustering analysis to recognization of viruses].

The pyrolysis gas chromatography of 29 strains nuclear polyhedrosis viruses, 11 strains cytoplasmic polyhedrosis viruses and 16 strains granulosis viruses were performed on a shimadzu GV-9A capillary gas chromatography equipped with a pyrolyser model PYR-2A. The differences among the inclusion bodies of NPV, CPV and GV could clearly be distinguished and also beidentified through analysis of fingerprinting the hierarchical clustering analysis of GC, This procedure has provided a usefel method for the classification and identification of viruses.

Algorithms↗

PubMed1992

Cluster analysis and related techniques in medical research.

In this paper we review methods of cluster analysis in the context of classifying patients on the basis of clinical and/or laboratory type observations. Both hierarchical and non-hierarchical methods of clustering are considered, although the emphasis is on the latter type, with particular attention devoted to the mixture likelihood-based approach. For the purposes of dividing a given data set into g clusters, this approach fits a mixture model of g components, using the method of maximum likelihood. It thus provides a sound statistical basis for clustering. The important but difficult question of how many clusters are there in the data can be addressed within the framework of standard statistical theory, although theoretical and computational difficulties still remain. Two case studies, involving the cluster analysis of some haemophilia and diabetes data respectively, are reported to demonstrate the mixture likelihood-based approach to clustering.

Algorithms↗

PubMed2000

Subgroups of children with autism by cluster analysis: a longitudinal examination.

OBJECTIVES: A hierarchical cluster analysis was conducted using a sample of 138 school-age children with autism. The objective was to examine (1) the characteristics of resulting subgroups, (2) the relationship of these subgroups to subgroups of the same children determined at preschool age, and (3) preschool variables that best predicted school-age functioning. METHOD: Ninety-five cases were analyzed. RESULTS: Findings support the presence of 2 subgroups marked by different levels of social, language, and nonverbal ability, with the higher group showing essentially normal cognitive and behavioral scores. The relationship of high- and low-functioning subgroup membership to levels of functioning at preschool age was highly significant. CONCLUSIONS: School-age functioning was strongly predicted by preschool cognitive functioning but was not strongly predicted by preschool social abnormality or severity of autistic symptoms. The differential outcome of the 2 groups shows that high IQ is necessary but not sufficient for optimal outcome in the presence of severe language impairment.

Autistic Disorder↗

PubMed2004

Sequential extracellular matrix-focused and baited-global cluster analysis of serial transcriptomic profiles identifies candidate modulators of renal tubulointerstitial fibrosis in murine adriamycin-induced nephropathy.

Transcriptome analysis using microarray technology represents a powerful unbiased approach for delineating pathogenic mechanisms in disease. Here molecular mechanisms of renal tubulointerstitial fibrosis (TIF) were probed by monitoring changes in the renal transcriptome in a glomerular disease-dependent model of TIF (adriamycin nephropathy) using Affymetrix (mu74av2) microarray coupled with sequential primary biological function-focused and secondary "baited"-global cluster analysis of gene expression profiles. Primary cluster analysis focused on mRNAs encoding matrix proteins and modulators of matrix turnover as classified by Onto-Compare and Gene Ontology and identified both molecules and pathways already implicated in the pathogenesis of TIF (e.g. transforming growth factor beta1-CTGF-fibronectin-1 pathway) and novel TIF-associated genes (e.g. SPARC and Matrilin-2). Specific gene expression patterns identified by primary extracellular matrix-focused cluster analysis were then used as bioinformatic bait in secondary global clustering, with which to search the renal transcriptome for novel modulators of TIF. Among the genes clustering with ECM proteins in the latter analysis were endoglin, clusterin, and gelsolin. In several notable cases (e.g. claudin-1 and meprin-1beta) the pattern of gene expression identified in adriamycin nephropathy in vivo was replicated during transdifferentiation of renal tubule epithelial cells to a fibroblast-like phenotype in vitro on exposure to transforming growth factor-beta and epidermal growth factor suggesting a role in fibrogenesis. The further exploration of these complex gene networks should shed light on the core molecular pathways that underpin TIF in renal disease.

Animals↗

PubMed2004

Resting functional MRI with temporal clustering analysis for localization of epileptic activity without EEG.

We report on the methods and initial findings of a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with temporal clustering analysis (TCA), for localizing interictal epileptic activity. Nine subjects were studied including six temporal lobe epilepsy (TLE) patients with confirmed localization indicated by successful seizure control after resection. The remaining three subjects had standard presurgical evaluations with inconsistent results or suspected extratemporal lobe foci. Peaks of activity, presumably epileptic, were detected in all nine subjects, using the resting functional MRI with temporal clustering analysis. In all six patients who underwent resective surgery, the fMRI with temporal clustering analysis accurately determined the epileptogenic hippocampal hemisphere (P = 0.005). In the three subjects without confirmed localization, the technique determined regions of activity consistent with those determined by the presurgical assessments. Though more studies are required to validate this technique, the results demonstrate the potential of the resting fMRI with temporal clustering technique to detect and localize epileptic activity without the need for simultaneous electroencephalography (EEG). The greatest potential benefit of this technique will be in the evaluation of patients with suspected extratemporal lobe epilepsy and patients whose standard assessments are discordant.

Adolescent↗

PubMed2004

A quantitative comparison of functional MRI cluster analysis.

The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.

Algorithms↗

PubMed2005

Global gene expression profiling and cluster analysis in Xenopus laevis.

We have undertaken a large-scale microarray gene expression analysis using cDNAs corresponding to 21,000 Xenopus laevis ESTs. mRNAs from 37 samples, including embryos and adult organs, were profiled. Cluster analysis of embryos of different stages was carried out and revealed expected affinities between gastrulae and neurulae, as well as between advanced neurulae and tadpoles, while egg and feeding larvae were clearly separated. Cluster analysis of adult organs showed some unexpected tissue-relatedness, e.g. kidney is more related to endodermal than to mesodermal tissues and the brain is separated from other neuroectodermal derivatives. Cluster analysis of genes revealed major phases of co-ordinate gene expression between egg and adult stages. During the maternal-early embryonic phase, genes maintaining a rapidly dividing cell state are predominantly expressed (cell cycle regulators, chromatin proteins). Genes involved in protein biosynthesis are progressively induced from mid-embryogenesis onwards. The larval-adult phase is characterised by expression of genes involved in metabolism and terminal differentiation. Thirteen potential synexpression groups were identified, which encompass components of diverse molecular processes or supra-molecular structures, including chromatin, RNA processing and nucleolar function, cell cycle, respiratory chain/Krebs cycle, protein biosynthesis, endoplasmic reticulum, vesicle transport, synaptic vesicle, microtubule, intermediate filament, epithelial proteins and collagen. Data filtering identified genes with potential stage-, region- and organ-specific expression. The dataset was assembled in the iChip microarray database, , which allows user-defined queries. The study provides insights into the higher order of vertebrate gene expression, identifies synexpression groups and marker genes, and makes predictions for the biological role of numerous uncharacterized genes.

Animals↗

PubMed1983

Classification of compounds by cluster analysis of Ames test data.

The dose-response curves for various compounds in the Ames test were analyzed by combining the least-squares method with cluster analysis. The following theoretical equation was employed: m(x) = (B + Sx)exp(-Tx), where m(x) is the number of revertants at the dose of x; B, the background number; S, the mutagenicity rate; and T, the toxicity rate. This equation is derived from a one-hit model modified to take account of the toxicity, and the parameters B, S and T are determined by applying the least-squares method for the fitting between theoretical and experimental dose-response curves with the use of the program package SALS (statistical analysis with least-squares fitting). With the values for B, S and T thus obtained, cluster analysis can clearly separate mutagenic and non-mutagenic compounds. Moreover, when cluster analysis into six groups is carried out, each group has quite characteristic parameter values. Consequently, this procedure is very useful for the classification of compounds according to the nature of the dose-response curve, that is, according to the nature of their mutagenic activity.

Carcinogens↗

PubMed1992

[Classification of allergens by positive percentage agreement and cluster analysis based on specific IgE antibodies in asthmatic children].

Classification and characterization of allergens is important because allergic patients are sensitized by a variety of allergens. One hundred and sixty-one sera from asthmatic children were investigated for specific IgE antibodies against 35 allergens including 20 inhalants and 15 foods by means of the MAST method. We assessed the allergenic properties of the allergens based on positive percentage agreement and cluster analysis. There was a high positive percentage agreement of specific IgE antibodies between house dust and Dermatophagoides spp., a relatively high agreement between 5 molds, cat and dog epithelium, mugwort and wormwood and 5 grasses. Among the food allergens, the positive percentage agreements were relatively high, especially between cow's milk, casein, cheese, and between 3 cereal grains. In the cluster analysis, house dust and Dermatophagoides spp. made a big cluster; therefore 32 allergens except house dust and mites were analyzed. From the results of the cluster analysis, the major cluster consisted of (1) ragweed, (2) mugwort and wormwood, (3) timothy, sweet vernal, velvet and cultivated rye, (4) wheat, barley and rice, (5) molds, (6) cow's milk, casein, soybean and cheese, (7) shrimp and crab, (8) egg white, (9) Japanese cedar, (10) dog epithelium, (11) cat epithelium. The cluster of grass pollens and cereal grains made one cluster. These results tend to confirm the presence of species cross-reactivities within the major classes of allergens.

Adolescent↗

PubMedDate not supplied

Spatial partitioning using multivariate cluster analysis and a contiguity algorithm.

Spatial analysis of epidemiological data can be a useful tool for identifying patterns of disease occurrence and can provide substantial support for prevention and control strategies. To obtain the greatest spatial resolution, it is important to use the smallest available areal units with homogeneous population. However, small areas usually have a small population, introducing spurious variability in the chosen indicators of disease occurrence. This paper describes an approach for combining small geographical units to stabilize mortality rates by pooling information across areas according to specified risk profiles. The procedure is based on a principal component analysis, followed by a cluster analysis of social-economic indicators to classify the risk profile of each small area. The classification is used in an algorithm to join neighbouring areas with similar profiles until an estimated population size is achieved. We applied this method to two Administrative Regions of the city of Rio de Janeiro, Brazil, using the census tracts as the basic areal unit. Census tracts were classified according to four socioeconomic categories distributed spatially as a mosaic, where tracts of differing categories neighbour each other. The aggregation algorithm produced a new partition of the region studied, with the created areal units preserving the internal socioeconomic homogeneity.

Adult↗