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Results for “cluster analysis”

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At least 937 records · Page 52Linked to original sources

PubMed2005

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

Bacteria↗

PubMed2007

A generalized analysis of hydrophobic and loop clusters within globular protein sequences.

BACKGROUND: Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. RESULTS: The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for alpha-helices or beta-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that beta-strands and especially alpha-helices are generally over-predicted within the limits of typical beta and alpha hydrophobic clusters. CONCLUSION: The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction.

Cluster Analysis↗

PubMed1999

[Chemical pattern recognition of traditional Chinese medicine xixin (II)].

The data matrix of macro and trace elements from 45 samples of traditional Chinese medicine Xixin were treated with principal component analysis and cluster analysis in order to establish why and how Xixin samples from different species can be recognized. According to the results of hierarchical clustering, three species of Xixin (Asarum heterotropoides Fr. var. mandshuricum (Maim.) Kitag., Asarum sieboldii Miq. var. seoulense Nakai and Asarum sieboldii Miq.) were classified and recognized accurately.

Aluminum↗

PubMed2001

Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies.

OBJECTIVE: To validate the use of cluster analysis for characterizing population dietary patterns. DESIGN: Cluster analysis was applied to a food frequency questionnaire to define dietary patterns. Independent estimates of nutrient intake were derived from 3-day food records. Heart disease risk factors were assessed using standardized protocols in a clinic setting. SETTING: Adult women (n = 1,828) participating in the Framingham Offspring-Spouse study. STATISTICAL ANALYSES: Age-adjusted mean nutrient intakes were determined for each cluster. Analysis of covariance was used to evaluate pairwise differences in intake across clusters. Compliance with published recommendations was determined for selected heart disease risk factors. Differences in age-adjusted compliance across clusters were evaluated using logistic regression. RESULTS: Cluster analysis identified 5 distinct dietary patterns characterized by unique food behaviors and significantly different nutrient intake profiles. Patterns rich in fruits, vegetables, grains, low-fat dairy, and lean protein foods resulted in higher nutrient density. Patterns rich in fatty foods, added fats, desserts, and sweets were less nutrient-dense. Women who consumed an Empty Calorie pattern were less likely to achieve compliance with clinical risk factor guidelines in contrast to most other groups of women. CONCLUSIONS: Cluster analysis is a valid tool for evaluating nutrition risk by considering overall patterns and food behaviors. This is important because dietary patterns appear to be linked with other health-related behaviors that confer risk for chronic disease. Therefore, insight into dietary behaviors of distinct clusters within a population can help to design intervention strategies for prevention and management of chronic health conditions including obesity and cardiovascular disease.

Aged↗

PubMed2001

Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks.

Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability.

Cluster Analysis↗

PubMed2005

Animal hair as biological indicator for heavy metal pollution in urban and rural areas.

Animal hair is a good biomonitoring tool for heavy metals assessment and reflects the content of heavy metals in the forage and soil. Heavy metals Fe, Mn, Co and Ni as well as toxic metals Cd and Pb were determined in goat, sheep and camel hair, forage and soil collected from four different environmental urban and rural regions. These regions are Aswan city farms, Allaqi desert pasture, Kalabsha farms and Halaiub desert pasture at far south of Egypt. The results reveal that sheep hair contains the highest levels of Fe and Mn (879 and 55 microg/g, respectively), camel hair contains the highest Pb, whereas for goat Cd and Ni were the highest. Heavy metal concentrations in the studied hair reflect the presence of these metals in the surrounding forage and soil and vary from one area to another, and give knowledge of pollution in the area. Correlation statistics analysis and cluster analysis show a good and significant value between metals in hair and plants.

Animals↗

PubMed2006

Applying pattern recognition methods to analyze the molecular properties of a homologous series of nitrogen mustard agents.

The purpose of this research was to analyze the pharmacological properties of a homologous series of nitrogen mustard (N-mustard) agents formed after inserting 1 to 9 methylene groups (-CH2-) between 2 -N(CH2CH2Cl)2 groups. These compounds were shown to have significant correlations and associations in their properties after analysis by pattern recognition methods including hierarchical classification, cluster analysis, nonmetric multi-dimensional scaling (MDS), detrended correspondence analysis, K-means cluster analysis, discriminant analysis, and self-organizing tree algorithm (SOTA) analysis. Detrended correspondence analysis showed a linear-like association of the 9 homologs, and hierarchical classification showed that each homolog had great similarity to at least one other member of the series-as did cluster analysis using paired-group distance measure. Nonmetric multi-dimensional scaling was able to discriminate homologs 2 and 3 (by number of methylene groups) from homologs 4, 5, and 6 as a group, and from homologs 7, 8, and 9 as a group. Discriminant analysis, K-means cluster analysis, and hierarchical classification distinguished the high molecular weight homologs from low molecular weight homologs. As the number of methylene groups increased the aqueous solubility decreased, dermal permeation coefficient increased, Log P increased, molar volume increased, parachor increased, and index of refraction decreased. Application of pattern recognition methods discerned useful interrelationships within the homologous series that will determine specific and beneficial clinical applications for each homolog and methods of administration.

Antineoplastic Agents, Alkylating↗

PubMed2003

A rainwater quality monitoring network: a preliminary study of the composition of rainwater in Galicia (NW Spain).

A network consisting of 17 sampling stations strategically distributed in the geographical area of Galicia (NW Spain) was set up in January 1999 in order to monitorice the ionic rainwater quality in this Spanish region which has an area approximately 29.575 km(2) and a population of 2.8 million. This paper presents the preliminary results of the exploratory study on the ionic rainfall composition in this area during the first year. In each rainwater sample collected chloride, sulphate, nitrite, nitrate, phosphate, sodium, potassium, magnesium, calcium and ammonium were measured, as were pH, conductivity and precipitation-volume. An univariate study according to the location of each sampling site was performed. Multivariate chemometric techniques, such as principal component analysis and cluster analysis, were applied to the complete data set (consisting of 630 observations) in order to elucidate the factors affecting the ionic composition of rainwater.

Cluster Analysis↗

PubMed1999

Interpretation of analytical data on n-alkanes and polynuclear aromatic hydrocarbons in Arbacia lixula from the coasts of Tenerife (Canary Islands, Spain) by multivariate data analysis.

The hydrocarbons contents (n-alkanes, polycyclic aromatic hydrocarbons) were determined in the sea urchin Arbacia lixula. Multivariate data analysis as principal component analysis, factor analysis and, cluster analysis were applied to elucidate sources of pollution. PCA and FA were performed to establish the relationships between variables (hydrocarbons), samples (sea urchin) and sources of pollution.

Alkanes↗

PubMed2002

Classification of Nebbiolo-based wines from Piedmont (Italy) by means of solid-phase microextraction-gas chromatography-mass spectrometry of volatile compounds.

Sixty-eight samples of wines from Piedmont (Italy) were analysed to determine their content of volatile compounds, using the solid-phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Samples were from five groups of wines: Barolo, Barbaresco, Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced from the Nebbiolo grape in the Langhe and Roero areas (province of Cuneo, Piedmont) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and few months) and production zone. Thirty-five analytes were identified; peak area data, corrected for internal standard, were used for pattern recognition treatments. Principal components analysis, hierarchical cluster analysis, Kohonen self organising map, stepwise linear discriminant analysis and soft independent modelling of class analogy were applied to the data, revealing a good separation between the five groups. A main factor, strictly connected to wine vintage, was identified and found to be related to some analytes.

Cluster Analysis↗

PubMed2006

Analysis of treatment effects on the microbial ecology of the human intestine.

A large number of studies have investigated gastrointestinal microbiota and changes in the gastrointestinal community. However, a concern in these studies is how best to assess changes in gastrointestinal community structure. This paper presents two different human trials where the fecal terminal restriction fragment length polymorphism data sets were analyzed to search for treatment effects. Principle components analysis and cluster analysis based on grouped data are compared with analysis of data by subject using distance coefficients. Comparison with baseline within an individual before grouping by treatment provided a clearer indication of treatment effects than did an evaluation of data grouped before analysis. In addition, a large within-subject sample size and multiple baseline samples are necessary to accurately analyze treatment effects.

Adult↗

PubMed1998

The use of multivariate statistical methods in psychiatry.

Multivariate methods such as factor analysis and cluster analysis have been used for many years by psychiatric researchers primarily for investigating the problems of psychiatric diagnosis. In more recent times, the range of multivariate techniques used has been extended as has the number of areas in which they are being applied.

Cluster Analysis↗

PubMed2005

Getting the big picture in community science: methods that capture context.

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.

Cluster Analysis↗

PubMed2006

Quality prediction of cell substrate using gene expression profiling.

Changes in cell culture conditions influence the metabolism of cells, which consequently affects the quality of the products that they produce, such as viral vectors, recombinant proteins, or vaccines. Currently there is no effective technique available to monitor global quality of cells in cell culture. Here we describe a new method using gene expression profiling by microarray to predict the quality of cell substrates. Human embryonic kidney 293 cells are a commonly used cell substrate in the production of biological products. We demonstrate that the yield of adenoviral vectors was lower in over-confluent 293 cells, compared to 40 or 90% confluent cells. Total RNA derived from these cells of different confluence states was reverse transcribed, labeled, and used to hybridize 10K cDNA arrays to determine biomarkers for confluence states. Phenotype scatter-plot analysis and cluster analysis were used for class discovery. Based on this approach, we identified genes that were either up-regulated or down-modulated in response to different cell confluence states. By multivariate predictive models we identified a set of 37 genes that were either down-regulated or up-regulated compared to 90% confluent cells as a predictor of cell confluence and quality of 293 cell cultures. The predictive accuracy of these models was assessed by the leave-one-out cross-validation method. The expression of selected gene predictors was validated by quantitative PCR analysis. Our results demonstrate that gene expression profiling can assess the quality of cell substrates prior to large-scale production of a biological product.

Adenoviridae↗

PubMed1995

Characterization of topographic EEG changes when smoking a cigarette.

The acute effects of cigarette smoking on the human electroencephalogram (EEG) were investigated by the topographic mapping technique. Twenty-six subjects participated in this study, which involved sham smoking and real smoking of preferred cigarettes. Effects of smoking were analyzed by statistical and multivariate analysis. Analysis of variance and t-test results showed a significant decrease in the theta and alpha 1 bands but a significant increase in the alpha 2, beta 1, and beta 2 bands. Factor analysis and cluster analysis showed that there were two or three independent regions on the scalp that indicate the effects of smoking on topographic EEG. A hypothesis was formed that smoking has different effects on human EEG profiles for different brain regions and that there are individual variations in the EEG responses to smoking.

Adult↗

PubMed2004

An atlas of differential gene expression during early Xenopus embryogenesis.

We have carried out a large-scale, semi-automated whole-mount in situ hybridization screen of 8369 cDNA clones in Xenopus laevis embryos. We confirm that differential gene expression is prevalent during embryogenesis since 24% of the clones are expressed non-ubiquitously and 8% are organ or cell type specific marker genes. Sequence analysis and clustering yielded 723 unique genes displaying a differential expression pattern. Of these, 18% were already described in Xenopus, 47% have homologs and 35% are lacking significant sequence similarity in databases. Many of them encode known developmental regulators. We classified 363 of the 723 genes for which a Gene Ontology annotation for molecular function could be attributed and found 'DNA binding' and 'enzyme' the most represented terms. The most common protein domains encoded in these embryonic, differentially expressed genes are the homeobox and RNA Recognition Motif (RRM). Fifty-nine putative orthologs of human disease genes, and 254 organ or cell specific marker genes were identified. Markers were found for nasal placode and archenteron roof, organs for which a specific marker was previously unavailable. Markers were also found for novel subdomains of various other organs. The tissues for which most markers were found are muscle and epidermis. Expression of cell cycle regulators fell in two classes, containing proliferation-promoting and anti-proliferative genes, respectively. We identified 66 new members of the BMP4, chromatin, endoplasmic reticulum, and karyopherin synexpression groups, thus providing a first glimpse of their probable cellular roles. Cluster analysis of tissues to measure tissue relatedness yielded some unorthodox affinities besides expectable lineage relationships. In conclusion, this study represents an atlas of gene expression patterns, which reveals embryonic regionalization, provides novel marker genes, and makes predictions about the functional role of unknown genes.

Animals↗

PubMed2005

Consequences of major accidents: assessing the number of injured people.

Quantitative risk assessment studies of accident scenarios usually involve estimating the number of fatalities that can be expected. The number of people injured, however, is seldom evaluated because it implies significant additional effort and often the information required to perform this evaluation is not available. However, the number of injured people can be very important for emergency planning, especially in relatively large accidents. In this paper, a set of 975 accidents were selected for analysis, with the aim of searching for a relationship between the number of people killed and the number of people injured. As the data were scattered, principal component analysis and clustering analysis were applied to identify the data subsets that could undergo a selective, specific statistical treatment. Further treatment of these subsets led to mathematical expressions that are used to estimate the probable number of injured people as a function of the number of fatalities for all accidents, as well as for gas cloud, fire and explosion events, respectively.

Accidents↗

PubMed2006

Detection and discrimination of phenol and primary alcohols in water using electronic noses.

Electronic nose methodology was used for discriminating between samples of n-primary alcohols and phenol in water. An electronic nose device developed at our laboratory was applied to detect the vapors from the samples. The device consists of 10 nonspecific gas sensors based on tin-dioxide, whose electrical conductivity change when exposed to the vapors. The whole set of responses constitutes a fingerprint associated with the sample for the particular array of sensors. Using this method, we could differentiate among n-primary alcohols, pure phenol, and aqueous dilutions of phenol. Dilutions of primary alcohols and phenol of 1, 3, 5, and 10% (v/v) were assayed. The fingerprints of the dilutions are similar to that of the respective pure (not diluted) alcohol, but with lower signal intensities. Principal component analysis and cluster analysis were performed in order to explore the feasibility of discriminating among the samples. Dilutions of phenol were successfully discriminated from those of primary alcohols. Discrimination of n-octanol and n-decanol from the other alcohols, including phenol, was also achieved. The results suggest that it is possible to discriminate among dilutions of phenol in water and to give an approximate gross estimation of phenol concentration in water.

Alcohols↗