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At least 541 records · Page 30Linked to original sources
PubMed2003

Cluster analysis of muscle functional MRI data.

Muscle functional magnetic resonance imaging (mfMRI) is frequently used to determine spatial patterns of muscle involvement in exercising humans. A frequent finding in mfMRI is that, even within synergistic muscle groups, signal intensity (SI) data from individual voxels can be quite heterogeneous. The purpose of this study was to develop a novel method for organizing heterogeneous mfMRI data into clusters whose members behave similarly to each other but distinctly from members of other clusters and apply it in studies of functional compartmentalization in the anterior compartment of the leg. An algorithm was developed that compared the SI time courses of adjacent voxels and grouped together voxels that were sufficiently similar. The algorithm's performance was verified by using simulated data sets with known regional differences in SI time courses that were then applied to experimental mfMRI data acquired from six male subjects (age 22.6 +/- 0.9 yr, mean +/- SE) who sustained isometric contractions of the dorsiflexors at 40% of maximum voluntary contraction. The experimental data were also characterized by using a traditional analysis (user-specified regions of interest from a single image), in which the relative change in SI and the contrast-to-noise ratio [CNR; 100%x(SI(RESTING) - SI(ACTIVE)/(noise standard deviation)] were measured. In general, clusters were found in areas in which the CNR exceeded 5. Cluster analysis made functional distinctions between regions of muscle that were not seen with traditional analysis. In conclusion, cluster analysis's use of the full SI time course provides more sensitivity to muscle functional compartmentation than traditional analysis.

Adult↗
PubMed2002

On-line on-chip post-column derivatisation reactions for pre-ionisation of analytes and cluster analysis in gradient micro-liquid chromatography/electrospray mass spectrometry.

A system is presented that demonstrates the principle of on-line and on-chip post-column derivatisation reactions in micro-high-performance liquid chromatography (micro-HPLC) hyphenated to electrospray time-of-flight mass spectrometry (ESI-TOFMS). In this micro-HPLC-chip-MS set-up, the analytes are separated using gradient micro-HPLC and subsequently derivatised on-chip and detected. One of the major limitations of MS detection is its dependency on the degree of ionisation, which is widely variable and compound-specific. Optimising and controlling the degree of ionisation in a simple manner would allow MS detection to be truly generic. One way of achieving this is by pre-ionisation of analytes using simple derivatisation procedures that are both rapid and quantitative. Performing this in situ on the system described here overcomes issues of sample handling and efficiency losses when time-consuming "bench chemistry" is necessary prior to analysis. The power of the system is demonstrated by the separation of primary and secondary amines, which are subsequently derivatised with a positively charged phosphonium complex and detected in an enhanced manner. Typically, molecular cations (M(+)) are detected showing that the ionisation process is dominated by the phosphonium species, leading to more constant ionisation for a variety of compounds. In addition, stable isotopically labelled ((12)C/(13)C)-phosphonium reagent is used for the reactions, allowing for inherent signal/noise (S/N) improvement and automated data processing using cluster analysis. A similar reaction scheme is used for the derivatisation of ketones and aldehydes, also demonstrating dramatic increases in sensitivity, especially with increasing temperature. Minimal loss in chromatographic fidelity in terms of retention times is observed by the introduction of the micromixer chip into the system. Optimal flow rates in micro-HPLC and ESI-MS are compatible with flow rates for the chip as well as a multitude of in-line optical detectors including UV and fluorescence. In addition, the micromixer chip can be positioned pre-column if preferred. The system is robust, easily fully automated and applicable to a wide variety of reactions. The system has a major advantage in its simple robust connection to the "normal scale" outside world.

Journal Article↗
PubMed2006

[Subtypes of specific language impairment in Spanish-speaking children: a cluster analysis of linguistic features].

INTRODUCTION: Specific language impairment (SLI) can be viewed as a continuum with different subtypes depending on the combination of deficits and strengths in each of the components of linguistic behavior. AIM. To analyze the phenomenological profiles in a correlative series of Spanish children in order to facilitate their endophenotypic differentiation and the choice of strategies of intervention. PATIENTS AND METHODS: 86 referred children, aged 4 to 9 years, with a mean non-verbal intelligence quotient of 93 (range 72-114) and accomplishing clinical criteria of SLI. Apart from clinical interview and neurological examination, a protocol of questionnaires and tests was used in all subjects in order to measure their formal (phonological, syntactic, lexical) and functional (semantic and pragmatic) linguistic abilities. A cluster analysis of variables was used in order to investigate SLI subtypes. RESULTS: In the total sample, a 24% of whole series have a pure phonological expressive disorder. 55% of subjects have mixed receptive/expressive disorders; from these, the most pervasive subtype is due to verbal agnosia (11%), but more frequently observed subtypes affect syntax reception and expression combined to difficulties in phonological programming (36%) or in lexical retrieval (8%). Furthermore, 21% of the total sample present with difficulties in pragmatic use of language even if their formal linguistic abilities and non-verbal behavior are normal. CONCLUSIONS: Our empirical approach confirms that the admitted SLI subtypes in international literature applies to Spanish-speaking children for endophenotyping and intervention-planning purposes.

Child↗
PubMedDate not supplied

C-myc amplification and cluster analysis in human gastric carcinoma.

The tumour samples ot 23 patients (9 male, 14 female, aged 28-85) were randomly selected for the study. DNA was isolated from paraffin embedded tissue for quantitative dot-blot hybridization, in order to determine the amplification values for the c-myc and K-ras oncogenes. The clinical and histological parameters studied were as follows: grade, TNM staging system, Lauren's histological type, localization and the severity of the disease. Amplified c-myc was found in 6 cases. Amplification was concomitant with c-myc overexpression detected with immunohistochemical staining. The amplification--9.1-fold on the average (ranging from 2.12 to 18.2) was significantly associated with the presence of distant metastasis (corr. coeff.: 0.5623, p < 0.01), but with none of the other parameters. No case with K-ras amplification was recorded. The result of the multivariate cluster analysis proved that age was the decisive factor in the segregation process. This age-related distribution (69 vs. 40, p < 0.001), however, did not coincide with either the incidence of distant metastasis or c-myc amplification.

Adenocarcinoma↗
PubMed2006

Hydrophobic cluster analysis and modeling of the human Rh protein three-dimensional structures.

Rh (Rhesus) is a major blood group system in man, which is clinically significant in transfusion medicine. Rh antigens are carried by an oligomer of two major erythroid specific polypeptides, the Rh (D and CcEe) proteins and the RhAG glycoprotein, that shared a common predicted structure with 12 transmembrane a-helices (M0 to M11). Non erythroid homologues of these proteins have been identified (RhBG and RhCG), notably in diverse organs specialized in ammonia production and excretion, such as kidney, liver and intestine. Phylogenetic studies and experimental evidence have shown that these proteins belong to the Amt/Mep/Rh protein superfamily of ammonium/methylammonium permease, but another view suggests that Rh proteins might function as CO2 gas channels. Until recently no information on the structure of these proteins were available. However, in the last two years, new insight has been gained into the structural features of Rh proteins (through the determination of the crystal structures of bacterial AmtB and archeaebacterial Amt-1. Here, models of the subunit and oligomeric architecture of human Rh proteins are proposed, based on a refined alignment with and crystal structure of the bacterial ammonia transporter AmtB, a member of the Amt/Mep/Rh superfamily. This alignment was performed considering invariant structural features, which were revealed through Hydrophobic Cluster Analysis, and led to propose alternative predictions for the less conserved regions, particularly in the N-terminal sequences. The Rh models, on which an additional Rh-specific, N-terminal helix M0 was tentatively positioned, were further assessed through the consideration of biochemical and immunochemical data, as well as of stereochemical and topological constraints. These models highlighted some Rh specific features that have not yet been reported. Among these, are the prediction of some critical residues, which may play a role in the channel function, but also in the stability of the subunit structure and oligomeric assembly. These results provide a basis to further understand the structure/function relationships of Rh proteins, and the alterations occurring in variant phenotypes.

Amino Acid Sequence↗
PubMed2001

Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

Analysis of Variance↗
PubMed1990

Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies.

A technique for automatically clustering linear envelopes of the EMG during gait has been developed which uses a temporal feature representation and a maximum peak matching scheme. This new technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest numbers of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification.

Algorithms↗
PubMed1995

Predictive validity of subtypes of chronic affective disorders derived by cluster analysis.

This study aimed to examine the predictive validity of the cluster-analytically derived 3 subtypes of chronic affective disorders that were proposed on the basis of cross-sectional symptoms and were shown to have some external validity with regard to psychosocial characteristics. Two-year naturalistic follow-up data were available for 38 (97%) of the original 39 patients with chronic affective disorders. The 3 subtypes, which were named psychotic subtype, late-onset female subtype and depressive personality subtype in the previous analyses, did not differ in their depressive severity or social functioning upon entry. In the course of the 2-year follow-up, however, there were significant differences in the Global Assessment of Functioning scores as well as the social adaptation in the economic sphere according to the Social Adjustment Scale-Self Report. All these indices revealed the psychotic subtype to have the worst outcome, followed by the depressive personality subtype, and the late-onset female subtype fared best.

Adult↗
PubMed2004

Salivary analyses and caries increment over 4 years: an approach by cluster analysis.

MAIN PROBLEM: Caries as a multifactorial process is influenced by salivary defense. Cluster analyses should give additional information on the role of salivary variables in relation to caries increment. METHODS: Samples of unstimulated and stimulated whole saliva from 28 young adults (mean age 23.5+/-2.1 years) were analyzed for flow rate, pH and buffer variables, lysozyme, lactoferrin, peroxidase, thiocyanate, secretory immunoglobulin A, and total protein. The decayed, missing, and filled surfaces (DMFS) were recorded at baseline and after 4 years. Cluster analyses were executed on the basis of salivary data. RESULTS: The mean caries increment (DeltaDMFS) over 4 years was 6.7+/-4.0 (range 1-16). In two-cluster processing, three out of four volunteers with low caries increments were grouped into one cluster. Only a few variables proved to be important for cluster characteristics. CONCLUSIONS: The results suggest that over 4 years (1) the volunteers with very low caries increment (DeltaDMFS=1) are classified always together, (2) these volunteers do not form a separate cluster by themselves, (3) low caries increment was related to higher salivary flow rate and lower levels of lysozyme and lactoferrin for unstimulated saliva and (4) the partial pressure of CO(2) was of importance in stimulated saliva.

Adult↗
PubMed2002

Analysis of the innate and adaptive phases of allograft rejection by cluster analysis of transcriptional profiles.

Both clinical and experimental observations suggest that allograft rejection is a complex process with multiple components that are, at least partially, functionally redundant. Studies using graft recipients deficient in various genes including chemokines, cytokines, and other immune-associated genes frequently produce a phenotype of delayed, but not indefinitely prevented, rejection. Only a small subset of genetic deletions (for example, TCR(alpha) or beta, MHC I and II, B7-1 and B7-2, and recombinase-activating gene) permit permanent graft acceptance suggesting that rejection is orchestrated by a complex network of interrelated inflammatory and immune responses. To investigate this complex process, we have used oligonucleotide microarrays to generate quantitative mRNA expression profiles following transplantation. Patterns of gene expression were confirmed with real-time PCR data. Hierarchical clustering algorithms clearly differentiated the early and late phases of rejection. Self-organizing maps identified clusters of coordinately regulated genes. Genes up-regulated during the early phase included genes with prior biological functions associated with ischemia, injury, and Ag-independent innate immunity, whereas genes up-regulated in the late phase were enriched for genes associated with adaptive immunity.

Animals↗
PubMed1991

Multivariate cluster analysis of pharmaceutical formulation data using Andrews plots.

A multivariate clustering technique known as the Andrews Plot was applied to a set of previously published data describing a series of pharmaceutical tablet formulations. Dependent variable data were subjected to a trigonometric transform algorithm and unique sinusoidal patterns were obtained for each of 18 formulations characterized by a total of 12 granulation and tablet parameters. Three main formulation clusters were observed in close agreement with a previously published analysis of the same data set using principal components methodology. Interpretation of cluster behavior lead to conclusions closely aligned to those of the original investigators, although some alternate hypotheses also arise. Limitations of the Andrews plotting technique were explored. The method appears to have value in the analysis of complex data sets derived from pharmaceutical formulation characterizations.

Algorithms↗
PubMed2000

Coupled two-way clustering analysis of gene microarray data.

We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

Cluster Analysis↗
PubMed2005

TA-clustering: cluster analysis of gene expression profiles through Temporal Abstractions.

This paper describes a new technique for clustering short time series of gene expression data. The technique is a generalization of the template-based clustering and is based on a qualitative representation of profiles which are labelled using trend Temporal Abstractions (TAs); clusters are then dynamically identified on the basis of this qualitative representation. Clustering is performed in an efficient way at three different levels of aggregation of qualitative labels, each level corresponding to a distinct degree of qualitative representation. The developed TA-clustering algorithm provides an innovative way to cluster gene profiles. We show the developed method to be robust, efficient and to perform better than the standard hierarchical agglomerative clustering approach when dealing with temporal dislocations of time series. Results of the TA-clustering algorithm can be visualized as a three-level hierarchical tree of qualitative representations and as such easy to interpret. We demonstrate the utility of the proposed algorithm on a set of two simulated data sets and on a study of gene expression data from S. cerevisiae.

Algorithms↗
PubMed2002

Cluster analysis of gene expression dynamics.

This article presents a Bayesian method for model-based clustering of gene expression dynamics. The method represents gene-expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters given the available data. The main contributions of this approach are the ability to take into account the dynamic nature of gene expression time series during clustering and a principled way to identify the number of distinct clusters. As the number of possible clustering models grows exponentially with the number of observed time series, we have devised a distance-based heuristic search procedure able to render the search process feasible. In this way, the method retains the important visualization capability of traditional distance-based clustering and acquires an independent, principled measure to decide when two series are different enough to belong to different clusters. The reliance of this method on an explicit statistical representation of gene expression dynamics makes it possible to use standard statistical techniques to assess the goodness of fit of the resulting model and validate the underlying assumptions. A set of gene-expression time series, collected to study the response of human fibroblasts to serum, is used to identify the properties of the method.

Bayes Theorem↗
PubMed2005

Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses.

Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.

Algorithms↗
PubMed2003

Self-organizing map for cluster analysis of a breast cancer database.

The purpose of this study was to identify and characterize clusters in a heterogeneous breast cancer computer-aided diagnosis database. Identification of subgroups within the database could help elucidate clinical trends and facilitate future model building. A self-organizing map (SOM) was used to identify clusters in a large (2258 cases), heterogeneous computer-aided diagnosis database based on mammographic findings (BI-RADS) and patient age. The resulting clusters were then characterized by their prototypes determined using a constraint satisfaction neural network (CSNN). The clusters showed logical separation of clinical subtypes such as architectural distortions, masses, and calcifications. Moreover, the broad categories of masses and calcifications were stratified into several clusters (seven for masses and three for calcifications). The percent of the cases that were malignant was notably different among the clusters (ranging from 6 to 83%). A feed-forward back-propagation artificial neural network (BP-ANN) was used to identify likely benign lesions that may be candidates for follow up rather than biopsy. The performance of the BP-ANN varied considerably across the clusters identified by the SOM. In particular, a cluster (#6) of mass cases (6% malignant) was identified that accounted for 79% of the recommendations for follow up that would have been made by the BP-ANN. A classification rule based on the profile of cluster #6 performed comparably to the BP-ANN, providing approximately 25% specificity at 98% sensitivity. This performance was demonstrated to generalize to a large (2177) set of cases held-out for model validation.

Adult↗
PubMed2006

Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series.

We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.

Algorithms↗