PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “cluster analysis”

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 19 recordsLinked to original sources
PubMed1978

Classification of suicide attempters by cluster analysis.

Cluster analytic procedures for classification were carried out on a sample of 236 suicide attempters. Rating variables concerned previous suicidal behaviour, details of the recent attempt and its motivation, mental state, and demographic characteristics. Results suggested the existence of three groups of attempters. The first comprised patients taking overdoses, on the whole showing less risk to life, less psychiatric disturbance, and more evidence of interpersonal rather than self-destructive motivation. The second groups, fewer in number, made severe attempts with more self-destructive motivation, by violent methods rather than overdose. The third and smallest group had a previous history of many attempts and gestures, made relatively mild attempts and were overtly hostile, engendering reciprocal hostility in the treating psychiatrist. These groups show some resemblance to those found in other studies.

Adult↗
PubMed1992

Detection of secondary structure elements in proteins by hydrophobic cluster analysis.

Hydrophobic cluster analysis (HCA) is a protein sequence comparison method based on alpha-helical representations of the sequences where the size, shape and orientation of the clusters of hydrophobic residues are primarily compared. The effectiveness of HCA has been suggested to originate from its potential ability to focus on the residues forming the hydrophobic core of globular proteins. We have addressed the robustness of the bidimensional representation used for HCA in its ability to detect the regular secondary structure elements of proteins. Various parameters have been studied such as those governing cluster size and limits, the hydrophobic residues constituting the clusters as well as the potential shift of the cluster positions with respect to the position of the regular secondary structure elements. The following results have been found to support the alpha-helical bidimensional representation used in HCA: (i) there is a positive correlation (clearly above background noise) between the hydrophobic clusters and the regular secondary structure elements in proteins; (ii) the hydrophobic clusters are centred on the regular secondary structure elements; (iii) the pitch of the helical representation which gives the best correspondence is that of an alpha-helix. The correspondence between hydrophobic clusters and regular secondary structure elements suggests a way to implement variable gap penalties during the automatic alignment of protein sequences.

Amino Acid Sequence↗
PubMedDate not supplied

Determination of liver volume from CT scans using histogram cluster analysis.

The histogram cluster analysis procedure (HICAP), which was developed by NASA for processing satellite images, classifies images into discrete clusters of pixels according to one or more arbitrary imaging variables. We incorporated this nonparametric, multivariate procedure in a semiautomatic computer algorithm for calculating total liver volume from CT scans and compared its performance with that of a human observer. Total liver volumes were calculated from CT scans in adult patients by the algorithm and by an experienced radiologist using the trackball controlled cursor at the CT console. Variability in the computer calculated volumes was determined by repeating calculations three times over the course of 3-12 months. Using HICAP in the univariate mode, we calculated total liver volumes from 28 contrast enhanced CT scans in 27 patients. Liver volumes calculated by the semiautomatic and manual methods had a median absolute difference of 3.6% (Vcomputer = 1.08 * Vmanual - 99.52 cc; r2 = 0.99). Median day-to-day variability of the computer calculated volumes was 1.9% (95% confidence interval: 1.3-2.7%). Using HICAP in a bivariate mode to illustrate its ability to incorporate two image features in one analysis, we studied an additional patient and compared total liver volume calculated from the univariate data set defined by the contrast enhanced CT scan with that calculated from the bivariate data set defined by nonenhanced and contrast enhanced CT scans. The HICAP errors were 4.1% in the univariate analysis and 0.4% in the bivariate analysis. It is concluded that this statistical clustering algorithm provides a clinically accurate, repeatable, and feasible method of in vivo liver volume determination.

Adult↗
PubMed1991

The magnocellular and parvocellular divisions of the monkey subthalamic nucleus as revealed by cluster analysis of neuronal sizes.

Cluster analysis of neuronal somal sizes in the subthalamic nucleus of rhesus monkeys from newborn to adult age allows the segregation of two territories with predominance of small and large cells, respectively. The topographic distribution of the 'parvocellular' and 'magnocellular' segments is similar when samples are obtained from coronal, horizontal and sagittal series of sections. The parvocellular component occupies the rostral pole, the entire rostrocaudal extent of the medial tip and dorsomedial border, and probably also the caudal cap. The magnocellular segment is in the central core extending to the ventrolateral border except for the medial tip. These findings and their correlation with the results of other morphologic and physiologic studies allow the following conclusions. (1) The monkey subthalamic nucleus contains at least two differentially distributed cell subpopulations. (2) The magnocellular division is more related to the pallido-subthalamic-pallidal loop involving the lateral pallidal segment. (3) The parvocellular division appears strategically located to control the pallidal output to diencephalic and mesencephalic targets. (4) Cluster analysis can reveal the existence of more than one neuronal population in a particular brain structure where an overall unimodal distribution of cell sizes may suggest the presence of a single type.

Aging↗
PubMed1992

Fuzzy cluster analysis of molecular dynamics trajectories.

We propose fuzzy clustering as a method to analyze molecular dynamics (MD) trajectories, especially of proteins and polypeptides. A fuzzy cluster analysis locates classes of similar three-dimensional conformations explored during a molecular dynamics simulation. The method can be readily applied to results from both equilibrium and nonequilibrium simulations, with clustering on either global or local structural parameters. The potential of this technique is illustrated by results from fuzzy cluster analyses of trajectories from MD simulations of various fragments of human parathyroid hormone (PTH). For large molecules, it is more efficient to analyze the clustering of root-mean-square distances between conformations comprising the trajectory. We found that the results of the clustering analysis were unambiguous, in terms of the optimal number of clusters of conformations, for the majority of the trajectories examined. The conformation closest to the cluster center can be chosen as being representative of the class of structures making up the cluster, and can be further analyzed, for example, in terms of its secondary structure. The CPU time used by the cluster analysis was negligible compared to the MD simulation time.

Amino Acid Sequence↗
PubMedDate not supplied

A cluster analysis of manic states.

A cluster analysis was performed on 81 manically disturbed patients assessed at interview on items of manic symptomatology and general psychopathology. Four groups were obtained: (1) a mildly excited group, (2) a group characterized by elation and speech disturbance, (3) a small severely disturbed excited group, more schizophrenic than manic, and (4) a group characterized by aggressive overactivity.

Aggression↗
PubMed1978

A note on cluster analysis and depression: disparities in results produced by the application of different clustering methods.

Cluster analysis is the most logically suited method for establishing psychiatric classifications. Different mathematical methods of clustering do, however, produce disparate results when applied to the same set of data. This study attempted to quantify the extent of such disparities, and found them to be marked. It was concluded that until cluster analysis has undergone further mathematical and statistical development, it should be used with caution.

Adult↗
PubMed1992

Cluster analysis in diagnosis.

The purpose of this paper is to survey the usefulness of cluster analysis in the special case of diagnoses. This complex topic is restricted, however, to the application on laboratory characteristics, separately or in connection with clinical data. The article is subdivided into three parts: (a) the fields of a possible use of cluster analysis, detection of diseases or subgroups of diseases, and data reduction by detection of structures; (b) a brief mathematical description of hierarchical and partitioning classification techniques (as a crucial point, the problems associated with these methods are discussed); (c) a critical review of 24 publications of the past 10 years concerning cluster analysis and diagnoses.

Clinical Laboratory Techniques↗
PubMed2026

Whole exome sequencing and cluster analysis reveal that EPB41L4A mutation may trigger tooth agenesis.

OBJECTIVE: To detect and analyze the correlation between commonly mutated genes and known genes associated with tooth agenesis in patients with non-syndromic tooth agenesis. The aim is to explore new genes that may be associated with tooth agenesis, to provide a genetic reference for its prevention as well as for the clinical diagnosis and treatment of tooth agenesis. METHODS: Genomic DNA was extracted from the peripheral blood of 18 congenitally edentulous subjects, and related gene mutations were identified by whole-exome sequencing. The genes related to maxillofacial development and the known pathogenic gene sequences of congenital tooth agenesis were selected for local alignment analysis of pairwise sequences, and the metric relationship of related sequences was determined. Hierarchical and fuzzy clustering methods were used for cluster analysis. RESULTS: Hierarchical clustering and fuzzy clusterings yielded consistent results. The EPB41L4A gene clustered with a large number of well-known and well-defined genes associated with tooth agenesis. From the perspective of cluster analysis, it can be inferred that the genes clustered together generally have similar functions. CONCLUSION: EPB41L4A, which is involved in the Wnt pathway, may be a candidate gene warranting further investigation.

Humans↗
PubMed1979

Standardization of measures prior to cluster analysis.

A common problem in cluster analysis is the determination of a scale-free measure of distance between individuals. This paper presents a procedure for scaling measurements using a reference individual as a standard of comparison. The procedure is particularly useful in classification of the results of laboratory procedures, where a reference standard is routinely produced. An example is the clustering of patterns that result from crossed antigen-antibody electrophoresis for determining the phenotype of the serum protein alpha 1-antitrypsin. The procedure appears to remove extraneous variability while retaining the information necessary for classification.

Blood Protein Electrophoresis↗
PubMed1992

DSM-III-R as a taxonomy. A cluster analysis of diagnoses and symptoms.

While there have been many applications of cluster analysis in psychiatric classification research, there are no studies in which cluster analysis is used to discover the taxonomic structure implicit in the DSM-III itself. In order to do so, the symptom index in the DSM-III-R manual was summarized in a two-way matrix of disorders by symptoms and then analyzed using a hierarchical classes model and companion algorithm (HICLAS) that permits overlap among classes. A novel feature of this model is that superordinate-subordinate relationships among diagnostic and symptom classes are explicitly represented. The HICLAS analysis revealed that there are several discrete symptom classes in DSM-III-R and that many psychiatric disorders can be modeled as combinations of one or more of these classes. The disorders associated with these symptom classes tend to fit the hierarchical classes model relatively well, particularly the mood disorders and the psychotic disorders. However, disorders such as adjustment, personality, and sexual disorder fit the model poorly or not at all. The results are in line with the conjecture that the taxonomic model implicit in DSM-III-R is a hybrid of discrete symptom classes and some other structure, perhaps a dimensional one.

Algorithms↗
PubMed1978

Entropy in the hierarchical cluster analysis of hospitals.

A new technique integrating concepts from cluster analysis and information theory was applied to the classification of Michigan hospitals. First, a number of cost-related variables that describe the hospitals and their surroundings were used in a cluster analysis to produce a hierarchy of classifications. Then for each classification, the within-group entropy was computed for each group of hospitals and averaged over the classification. Finally, this average entropy was used as an aid to judgment in deciding which of the many classifications in the hierarchy yields the most reasonable groupings of hospitals.

Blue Cross Blue Shield Insurance Plans↗
PubMed1979

Application of cluster analysis for characterization of spatial distribution of particles by stereological methods.

A method for the detection and characterization of clusters of particles observed in section with the electron microscope is presented. Cluster analysis is performed by the division method described by Berthet et al. (1976). Starting from a single cluster, profiles from each electron micrograph are successively classified in sets containing an increasing number of clusters. The decrease in the mean free distance, lambda, between profiles in the clusters, is used for terminating the subdivision procedure. The function relating the mean free distance with the number of clusters is evaluated in each subdivision set. The actual number of clusters is selected on the basis of the slope of that function, at a point where lambda has a value close to the average profile diameter. The method assumes a convex shape for the clusters; the salient feature is that it provides a physical delineation of clusters in the section. Hence, an evaluation of some characteristics of clusters in the three-dimensional sample may be obtained by using standard stereological procedures. Characterization of the volume to which the individual particles of a population are eventually restricted can as a result be performed. Practical problems in the acquisition of the data needed for cluster analysis are discussed and a system using for that purpose a Quantimet 720 image analyser in a basic configuration, connected on line with a PDP 11/10 minicomputer, is presented. Application of the method is illustrated by the analysis of lysosomes in cultured hepatoma (HTC) cells, at the end of mitosis and during the S phase. Cluster analysis shows that in cells actively synthesizing DNA they are grouped in clusters representing 5.7% of the cellular volume. Moreover, the average number of particles per cluster falls from a minimum of thirteen at mitosis to only six at the S phase.

Cells, Cultured↗
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↗
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↗
PubMed1991

Structural symmetry of the extracellular domain of the cytokine/growth hormone/prolactin receptor family and interferon receptors revealed by hydrophobic cluster analysis.

Sequence comparison based on Hydrophobic Cluster Analysis procedures shows that the extracellular approximately 200 amino acids domains of cytokines receptors belonging to the Cytokine/Growth hormone/Prolactin receptor family and to the Interferon one are organized in two homologous subdomains. Further, comparison of the subdomains of 32 independent sequences and of a lot of already recognized homologous domains with data bases could lead to the hypothesis that these approximately 100 amino acids subdomains could possess the overall fold of the constant immunoglobulin domains and so could belong to the immunoglobulin superfamily.

Amino Acid Sequence↗