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PubMed1995

An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

With a growing number of structures available in the Brookhaven Protein Data Bank, automatic methods for domain identification are required for the construction of databases. Domains are considered to be clusters of secondary structure elements. Thus, helices and strands are first clustered using intersecondary structural distances between C alpha positions, and dendrograms based on this distance measure are used to identify domains. Individual domains are recognized by a disjoint factor, which enables the automatic identification and classification into disjoint, interacting, and conjoint domains. Application to a database of 83 protein families and 18 unique structures shows that the approach provides an effective delineation of boundaries and identifies those proteins that can be considered as a single domain. A quantitative estimate of the interaction between domains has been proposed. The database of protein domains is a useful tool for understanding protein folding, for recognizing protein folds, and for understanding structure-activity relationships.

Algorithms↗
PubMed2000

Fuzzy clustering analysis of the first 10 MEIC chemicals.

In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests.

Algorithms↗
PubMed1996

Cluster analysis of MCMI scores of suicidal psychiatric patients: four personality profiles.

Millon Clinical Multiaxial Inventory (MCMI) scores from 299 suicidal psychiatric outpatients were cluster analyzed in hopes of identifying clinical subgroups and deriving specific treatment indications. The analysis revealed four personality profiles: Negativistic/Avoidant/Schizoid, Avoidant/Dependent/ Negativistic, Antisocial, and Histrionic/Narcissistic. This grouping was validated by examining demographics, diagnoses, and scores on several psychometric scales. Results showed few differences in demographics, diagnosis, or intelligence, but significant differences in depression severity, hopelessness, anxiety, problem-solving self-appraisal, and alcohol abuse. Implications for tailoring interventions for subtypes of suicidal patients are discussed.

Adolescent↗
PubMed1999

Improved signal-to-noise ratio in parametric images by cluster analysis.

Parametric images are formed by analyzing the concentration history of every voxel in PET data sets. Because PET concentration data at the voxel level are rather noisy, noise propagation into the parametric image is often quite noticeable. To address this problem, a model-based clustering method has been developed to generate parametric images. The basic idea of the clustering method is to average over voxels whose concentration histories have the same shape. We applied the method to a two-parameter (K1, k2) compartment model of local cerebral blood flow. The statistic R = integral tC(t) dt/integral C(t) dt= integral te-k2t multiply sign in circle Ca(t) dt/integral e-k2t multiply sign in circle Ca(t) dt classifies curves in terms of k2, where C(t) and Ca(t) denote the tissue and blood concentration histories, respectively, and multiply sign in circle is the convolution operator. Simulation studies of noise propagation in the clustering statistic showed that 30% voxel noise yielded a 2% standard deviation in R. Parametric images of blood flow and partition coefficient were computed for an O15 study, with and without clustering. Cluster size affected bias, statistical precision, and computation time. With clusters of 400 voxels, the variance of the flow parameter was around 1/50 smaller with clustering, with negligible bias and a computation time of 30 s on a 64-MHz workstation for 15 x 128 x 128 images with MATLAB 5.1.

Adult↗
PubMed2006

Detection and identification of protein isoforms using cluster analysis of MALDI-MS mass spectra.

We describe an approach to screen large sets of MALDI-MS mass spectra for protein isoforms separated on two-dimensional electrophoresis gels. Mass spectra are matched against each other by utilizing extracted peak mass lists and hierarchical clustering. The output is presented as dendrograms in which protein isoforms cluster together. Clustering could be applied to mass spectra from different sample sets, dates, and instruments, revealed similarities between mass spectra, and was a useful tool to highlight peptide peaks of interest for further investigation. Shared peak masses in a cluster could be identified and were used to create novel peak mass lists suitable for protein identification using peptide mass fingerprinting. Complex mass spectra consisting of more than one protein were deconvoluted using information from other mass spectra in the same cluster. The number of peptide peaks shared between mass spectra in a cluster was typically found to be larger than the number of peaks that matched to calculated peak masses in databases, thus modified peaks are probably among the shared peptides. Clustering increased the number of peaks associated with a given protein.

Arabidopsis↗
PubMed1999

Flow cytometric immunophenotyping of mature lymphatic neoplasias using knowledge guided cluster analysis.

Flow cytometry is widely used for the immunological characterization of hematopoietic malignancies. Discrimination of normal and malignant cellular immunophenotypes is the most critical step in data analysis, especially if multi-color analysis is performed on highly heterogenous cell suspensions. We therefore investigated, whether adaptive, simultaneous multiparameter gating allowed automated, operator independent analysis of data obtained from the immunophenotyping of blood or bone marrow samples with regard to the presence of non-Hodgkin lymphoma cells. The identification of physiological and malignant cells was achieved by predefining population boundaries, based on the expectations of the population's location in two-dimensional dot plots. The prospective application of these predefined region boundaries in 52 blood and bone marrow samples enabled identification of lymphoma cells with regard to their presence and immunophenotype, based on the correlation of markers as defined in multiple tubes. Our data confirm that highly standardized data analysis methods can reduce the variability of analysis and support the expert in establishing a rapid classification of the sample.

B-Lymphocytes↗
PubMed1991

Cluster analysis of clinical data measured in the surgical intensive care unit.

A set of 13 extensively used hemodynamic, ventilatory and gas analysis variables are measured (on-line or off-line) on 200 patients in an intensive care unit (ICU) during the 6 h immediately following cardiac surgery. In order to identify both low- and high-risk patterns, a clustering method is applied to these data at three equidistant observation times. Application of the divergence criterion allows a quantitative evaluation of the diversity between the clusters identified, showing that the two patterns are really distinct in the 13-D space. The same criterion is then used to find possible subsets of variables capable of maintaining, in time, an effective separation power. The latter always include the cardiac index (CI), representative of cardiac performance, and two indices related to respiratory efficiency and metabolic rate, i.e., the carbon dioxide production index (VCO2I) and the arterio-venous oxygen difference (avO2D).

Cardiac Surgical Procedures↗
PubMed2001

[Aggressive and suicidal behavior. A cluster analysis study of suicidal and nonclinical subjects].

AIMS: An attempt was made to examine the relationship between different dimensions of aggression and suicidal behavior in clinical and non-clinical subjects. METHOD: A clinical sample of 68 suicide attempters (39 females, 29 males) and a matched sample of 70 non-clinical subjects (38 females, 32 males) were subgrouped by cluster analytic techniques. Classification variables included the scales of the Aggressive Factors Questionnaire (AFQ). Discriminant function analyses were performed on the clusters to determine the quality of group separation and to assess the power of each input variable. External variables such as the amount of hopelessness, suicidal ideation and the number of suicide attempts were used to assess the clinical relevance of the identified clusters. RESULTS: Two subgroups were identified in each sample. Variables that contributed most to group separation were Auto-aggression/Depression and Spontaneous Aggression in the non-clinical sample and Excitability in the clinical sample, respectively. The clusters identified within the non-clinical sample differentiated between ideators and non-ideators. Cluster allocation within the clinical sample was indicative of the number of suicide attempts. CONCLUSIONS: Clusters defined on the basis of different dimensions of aggressiveness turned out to be a classification with significant implications for the assessment of suicidal behavior.

Adolescent↗
PubMedDate not supplied

[Estimation of localization and dipole moment of alpha- and theta-rhythm sources by cluster analysis in healthy subjects and schizophrenics].

In 12 healthy subjects and 9 schizophrenic patients in the background conditions (with eyes closed) EEG was recorded from 16 standard derivations (10-20 system) during 3 min. The record underwent the spectral analysis detecting alpha- and theta-frequency bands. After the preliminary narrow band filtration for the main frequencies the sources of the spontaneous rhythms were localized. The data on localization for all healthy subjects and patients were summarized. The K-means clustering was used for identification of the sources clusters which were revealed in occipital and parietal lobes and limbic cortex for alpha-rhythm and also in frontal, temporal and parietal regions, limbic cortex and hippocampus for theta-rhythm. In schizophrenic patients in comparison with healthy subjects there was revealed significant increase of the numbers of dipole sources of alpha-rhythm in the clusters localized in limbic cortex and hippocampus. For theta-rhythm there was significant increase of the dipole moment of the sources in the clusters localized in the temporal and frontal cortices and hippocampus in patients in comparison with the norm.

Adolescent↗
PubMed2004

[Cluster analysis and identify significance of novel genes related to glioma in molecule classification].

OBJECTIVE: To screen differentially expressed genes in the development of human glioma and establish molecular classification of glioma preliminary based on gene expression using cDNA microarray. METHODS: Brain specimens were obtained from 18 patients with glioma, 10 males and 8 females, aged 14 approximately 62 with an average age of 44.4. The total RNAs of these glioma specimens and 2 specimens of donated brain of normal adults were extracted. BioStarH140S microarray (including 8347 old genes and 5592 novel genes) were adopted and hybridized with probes which were prepared from the total RNAs. Differentially expressed genes between the normal tissues and glioma tissues were assayed after scanning cDNA microarray with ScanArray 4000. Northern hybridization, and in situ hybridization (ISH) were used to identify the functions of novel genes. Those differentially expressed genes were studied with Hierarchical method and molecular classification of glioma was preliminarily carried out. RESULTS: Among the 13 939 target genes, there were 1200 (8.61%) differentially expressed genes and 395 (2.83%) novel genes. 348 genes were up-regulated and 852 genes were down-regulated in glioma. The results of bioinformatical analysis, Northern hybridization and ISH revealed that those novel genes were highly associated with glioma. There were multiple genes which were relevance to classification by Hierarchical method, such as MAP gene, cytoskeleton and matrix motility genes, etc. Molecular classification of glioma with Hierarchical cluster was in accordance with pathology and revealed internal essence in tumorigenesis and development. CONCLUSION: Multiple genes play important roles in development of glioma. cDNA microarray technology is a powerful technique in screening for differentially expressed genes between two different kinds of tissues.

Adolescent↗
PubMed1999

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.

Oligonucleotide arrays can provide a broad picture of the state of the cell, by monitoring the expression level of thousands of genes at the same time. It is of interest to develop techniques for extracting useful information from the resulting data sets. Here we report the application of a two-way clustering method for analyzing a data set consisting of the expression patterns of different cell types. Gene expression in 40 tumor and 22 normal colon tissue samples was analyzed with an Affymetrix oligonucleotide array complementary to more than 6,500 human genes. An efficient two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues. Coregulated families of genes clustered together, as demonstrated for the ribosomal proteins. Clustering also separated cancerous from noncancerous tissue and cell lines from in vivo tissues on the basis of subtle distributed patterns of genes even when expression of individual genes varied only slightly between the tissues. Two-way clustering thus may be of use both in classifying genes into functional groups and in classifying tissues based on gene expression.

Adenocarcinoma↗
PubMed1992

A program for the application of the radial distribution function to cluster analysis in cell biology.

The radial distribution function g(r) is one measure of spatial pattern commonly used in statistical physics to analyze the structure of liquids and has been used in several cellular systems. The graphs of the radial distribution function present three different functional forms. The first form indicates a random distribution; in the second form the graph is characteristic of the cluster distribution; and the third type is characteristic of substantial order. The Funct-G program uses the coordinates (x,y) of each point on m photographs to calculate the radial distribution function g(r) and produce a histogram to analyze graphically this function and to define the distribution model.

Cell Biology↗