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PubMedDate not supplied

A personality typology of smokers.

The study of smoker's personality has a long and controversial history. Smokers tend to be more extroverted, tense, and anxious and have more antisocial characteristics than nonsmokers. However, some of the data is contradictory, and the strength of the relationship between personality and smoking is weak, probably because smokers are not a homogeneous group. To test this possibility, we used cluster analysis to identify types of smokers in a general population sample. Using Ward's heirarchical clustering algorithm, two clusters were identified from the scores on 10 personality measures in 346 smokers. The smaller cluster (28.8% of the smokers) was higher on neuroticism-related characteristics. The clusters were not different on extroversion-related measures, but people in the smaller cluster were more likely to have symptoms of alcoholism and to have more drinking-related problems (although they didn't actually drink more). They were also younger, had lower income, and were antisocial. The pattern of results suggests that there is a subpopulation of smokers who are more anxious than other smokers and who have strong antisocial tendencies.

Adult↗
PubMed2005

An evolving neural network for the interpretation of gene expression patterns.

It is of central interest in biology to understand how gene activity networks are coordinated and integrated in the cell. Within the field of genomics, microarray technologies have become a powerful technique for monitoring simultaneously the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods that can suggest which groups of genes are activated by similar conditions. We review several techniques based on self-organizing map and clustering algorithms but implemented through a network of units controlled by biologically inspired functions (see Table 1). The computer tool, named NBIA, permits a categorization that generates a set of gene groups with coordinated expression patterns.

Algorithms↗
PubMed2002

Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering.

MOTIVATION: Microarray images challenge existing analytical methods in many ways given that gene spots are often comprised of characteristic imperfections. Irregular contours, donut shapes, artifacts, and low or heterogeneous expression impair corresponding values for red and green intensities as well as their ratio R/G. New approaches are needed to ensure accurate data extraction from these images. RESULTS: Herein we introduce a novel method for intensity assessment of gene spots. The technique is based on clustering pixels of a target area into foreground and background. For this purpose we implemented two clustering algorithms derived from k-means and Partitioning Around Medoids (PAM), respectively. Results from the analysis of real gene spots indicate that our approach performs superior to other existing analytical methods. This is particularly true for spots generally considered as problematic due to imperfections or almost absent expression. Both PX(PAM) and PX(KMEANS) prove to be highly robust against various types of artifacts through adaptive partitioning, which more correctly assesses expression intensity values. AVAILABILITY: The implementation of this method is a combination of two complementary tools Extractiff (Java) and Pixclust (free statistical language R), which are available upon request from the authors.

Algorithms↗
PubMed2004

Gene expression analysis on biochemical networks using the Potts spin model.

MOTIVATION: Microarray technology allows us to profile the expression of a large subset or all genes of a cell. Biochemical research over the last three decades has elucidated an increasingly complete image of the metabolic architecture. For less complex organisms, such as Escherichia coli, the biochemical network has been described in much detail. Here, we investigate the clustering of such networks by applying gene expression data that define edge lengths in the network. RESULTS: The Potts spin model is used as a nearest neighbour based clustering algorithm to discover fragmentation of the network in mutants or in biological samples when treated with drugs. As an example, we tested our method with gene expression data from E.coli treated with tryptophan excess, starvation and trpyptophan repressor mutants. We observed fragmentation of the tryptophan biosynthesis pathway, which corresponds well to the commonly known regulatory response of the cells.

Algorithms↗
PubMedDate not supplied

Cluster analyses of cardiovascular responsivity to three laboratory stressors.

Seventy-three young normotensive male subjects were tested with an experimental protocol that included a reaction time, a mental arithmetic, and a cold pressor task. Physiological variables that were recorded included heart rate, stroke volume, pre-ejection period, blood pressure, total peripheral resistance, and respiratory sinus arrhythmia. In order to identify subgroups of subjects who differed in their pattern of autonomic responses to the tasks, the physiological change scores from baseline to the tasks for each subject were entered into a cluster analysis for each task. Ward's method was used as the clustering algorithm. The cluster analyses identified four clusters for the reaction time and mental arithmetic tasks, and five clusters for the cold pressor task. Although there was a wide range of patterns exhibited by cluster subgroups, most subjects who were reactive to the tasks showed response patterns that were qualitatively similar to the pattern of overall mean response by all subjects, albeit varying considerably in terms of quantitative response. Little evidence was generated for the consistency of extreme beta-adrenergic response from one task to another, although significant consistency was noted when milder beta-responders were included in the comparisons. Some consistency of alpha-adrenergic response noted across tasks, as well as significant consistency of being relatively nonreactive to the tasks.

Adult↗
PubMed2003

Clustering of microarray data reveals transcript patterns associated with somatic embryogenesis in soybean.

Globular somatic embryos can be induced from immature cotyledons of soybean (Glycine max L. Merr. cv Jack) placed on high levels of the auxin 2,4-dichlorophenoxyacetic acid (2,4-D). Somatic embryos develop from the adaxial side of the cotyledon, whereas the abaxial side evolves into a callus. Using a 9,280-cDNA clone array, we have compared steady-state RNA from the adaxial side from which embryos develop and from the abaxial callus at five time points over the course of the 4 weeks necessary for the development of globular embryos. In a second set of experiments, we have profiled the expression of each clone in the adaxial side during the same period. A total of 495 genes differentially expressed in at least one of these experiments were grouped according to the similarity of their expression profiles using a nonhierarchical clustering algorithm. Our results indicate that the appearance of somatic embryos is preceded by dedifferentiation of the cotyledon during the first 2 weeks on auxin. Changes in mRNA abundance of genes characteristic of oxidative stress and genes indicative of cell division in the adaxial side of the cotyledons suggest that the arrangement of the new cells into organized structures might depend on a genetically controlled balance between cell proliferation and cell death. Our data also suggest that the formation of somatic globular embryos is accompanied by the transcription of storage proteins and the synthesis of gibberellic acid.

2,4-Dichlorophenoxyacetic Acid↗
PubMed1997

A fuzzy clustering approach to EP estimation.

The problem of extracting a useful signal (a response) buried in relatively high amplitude noise has been investigated, under the conditions of low signal-to-noise ratio. In particular, we present a method for detecting the "true" response of the brain resulting from repeated auditory stimulation, based on selective averaging of single-trial evoked potentials. Selective averaging is accomplished in two steps. First, an unsupervised fuzzy-clustering algorithm is employed to identify groups of trials with similar characteristics, using a performance index as an optimization criterion. Then, typical responses are obtained by ensemble averaging of all trials in the same group. Similarity among the resulting estimates is quantified through a synchronization measure, which accounts for the percentage of time that the estimates are in phase. The performance of the classifier is evaluated with synthetic signals of known characteristics, and its usefulness is demonstrated with real electrophysiological data obtained from normal volunteers.

Algorithms↗
PubMed2006

Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering.

A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).

Activities of Daily Living↗
PubMed2001

Implementing multiple static field delivery for intensity modulated beams.

A clinically oriented two-dimensional intensity-modulated beam delivery method is implemented using multiple static segmented fields, i.e., the "step-and-shoot" approach. Starting with a desired al" intensity distribution, it creates a multiple-level intensity approximation, and then constructs a sequence of segmented fields to deliver the multiple-level intensities using multileaf collimator (MLC) and independent backup jaws. The approach starts with a simple grouping of all the nonzero intensity values into a minimum number of clusters for a user specified deviation tolerance for the ideal plan. The k-means clustering algorithm is then employed to find the optimal levels of intensity that minimize the discrepancies between the ideal and the approximated intensities, without violating the user specified deviation tolerance. The multiple-level intensities are then decomposed into a sequence of machine deliverable segments. Apart from the first segment for each gantry angle, all the other segments are arranged to minimize the total travel distance of the leaves. The first segment covers the entire irradiated area and is used for treatment verification by electronic portal imaging. The implementation issues due to the physical constraints of the MLCs are also addressed.

Algorithms↗
PubMed2006

Human population genetic structure and diversity inferred from polymorphic L1(LINE-1) and Alu insertions.

BACKGROUND/AIMS: The L1 retrotransposable element family is the most successful self-replicating genomic parasite of the human genome. L1 elements drive replication of Alu elements, and both have had far-reaching impacts on the human genome. We use L1 and Alu insertion polymorphisms to analyze human population structure. METHODS: We genotyped 75 recent, polymorphic L1 insertions in 317 individuals from 21 populations in sub-Saharan Africa, East Asia, Europe and the Indian subcontinent. This is the first sample of L1 loci large enough to support detailed population genetic inference. We analyzed these data in parallel with a set of 100 polymorphic Alu insertion loci previously genotyped in the same individuals. RESULTS AND CONCLUSION: The data sets yield congruent results that support the recent African origin model of human ancestry. A genetic clustering algorithm detects clusters of individuals corresponding to continental regions. The number of loci sampled is critical: with fewer than 50 typical loci, structure cannot be reliably discerned in these populations. The inclusion of geographically intermediate populations (from India) reduces the distinctness of clustering. Our results indicate that human genetic variation is neither perfectly correlated with geographic distance (purely clinal) nor independent of distance (purely clustered), but a combination of both: stepped clinal.

Alu Elements↗
PubMed2006

CoXpress: differential co-expression in gene expression data.

BACKGROUND: Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed. RESULTS: We have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes. CONCLUSION: coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.

Algorithms↗
PubMed2001

Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays.

Using an oligonucleotide array containing sequences complementary to approximately 3200 full-length human cDNAs and 3400 expressed sequence tags (GeneChip, Affymetrix), mRNA expression patterns were probed in 18 colon adenocarcinomas and 4 adenomas. Paired normal tissue was available and analyzed for each of the tumors. Relatively few changes in transcript expression are associated with colon cancer. Nineteen transcripts (0.48% of those detected) had at least 4-10.5-fold higher mRNA expression in carcinoma compared with paired normal samples, whereas 47 transcripts (1.3% of those detected) had at least 4-38-fold or lower expression in the tumor tissue compared with the normal samples. Some of these differences were confirmed by reverse transcription-PCR. Many of these transcripts were already known to be abnormally expressed in neoplastic tissue in general, or colon cancer in particular, and several of these differences were also observed in premalignant adenoma samples. A two-way hierarchical clustering algorithm successfully distinguished adenoma from adenocarcinoma and normal tissue, generating a phylogenetic tree that appropriately represented the clinical relationship between the three tissue types included in the analysis. This supports the concept that genome-wide expression profiling may permit a molecular classification of solid tumors.

Adenocarcinoma↗
PubMedDate not supplied

Knowledge-assisted recognition of cluster boundaries in gene expression data.

BACKGROUND AND MOTIVATION: DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using DNA microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and further lead to the development of new disease diagnosis technologies. An important step in the analysis of gene expression data is to classify genes with similar expression patterns into the same groups. To this end, hierarchical clustering algorithms have been widely used. Major advantages of hierarchical clustering algorithms are that investigators do not need to specify the number of clusters in advance and results are presented visually in the form of a dendrogram. However, since traditional hierarchical clustering methods simply provide results on the statistical characteristics of expression data, biological interpretations of the resulting clusters are not easy, and it requires laborious tasks to unveil hidden biological processes regulated by members in the clusters. Therefore, it has been a very difficult routine for experts. OBJECTIVE: Here, we propose a novel algorithm in which cluster boundaries are determined by referring to functional annotations stored in genome databases. MATERIALS AND METHODS: The algorithm first performs hierarchical clustering of gene expression profiles. Then, the cluster boundaries are determined by the Variance Inflation Factor among the Gene Function Vectors, which represents distributions of gene functions in each cluster. Our algorithm automatically specifies a cutoff that leads to functionally independent agglomerations of genes on the dendrogram derived from similarities among gene expression patterns. Finally, each cluster is annotated according to dominant gene functions within the respective cluster. RESULTS AND CONCLUSIONS: In this paper, we apply our algorithm to two gene expression datasets related to cell cycle and cold stress response in budding yeast Saccharomyces cerevisiae. As a result, we show that the algorithm enables us to recognize cluster boundaries characterizing fundamental biological processes such as the Early G1, Late G1, S, G2 and M phases in cell cycles, and also provides novel annotation information that has not been obtained by traditional hierarchical clustering methods. In addition, using formal cluster validity indices, high validity of our algorithm is verified by the comparison through other popular clustering algorithms, K-means, self-organizing map and AutoClass.

Algorithms↗
PubMed2006

Hybrid approach of selecting hyperparameters of support vector machine for regression.

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.

Algorithms↗
PubMed2002

Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees.

MOTIVATION: Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem. RESULTS: This paper describes a new framework for representing a set of multi-dimensional gene expression data as a Minimum Spanning Tree (MST), a concept from the graph theory. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXpression data Clustering Analysis and VisualizATiOn Resource (EXCAVATOR). To demonstrate its effectiveness, we have tested it on three data sets, i.e. expression data from yeast Saccharomyces cerevisiae, expression data in response of human fibroblasts to serum, and Arabidopsis expression data in response to chitin elicitation. The test results are highly encouraging. AVAILABILITY: EXCAVATOR is available on request from the authors.

Algorithms↗
PubMed2001

Minimum spanning trees for gene expression data clustering.

This paper describes a new framework for microarray gene-expression data clustering. The foundation of this framework is a minimum spanning tree (MST) representation of a set of multi-dimensional gene expression data. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXCAVATOR. To demonstrate its effectiveness, we have tested it on two data sets, i.e., expression data from yeast Saccharomyces cerevisiae, and Arabidopsis expression data in response to chitin elicitation.

Algorithms↗
PubMed2006

Subdimension-based similarity measure for DNA microarray data clustering.

Microarray data analysis is useful for understanding biological processes. A number of clustering algorithms have been used to achieve this task. However, the performance of these methods can be significantly degraded due to the presence of nonsignificant conditions. In this paper, we propose a robust clustering algorithm based on a similarity measure. The key concept of the proposed similarity measure is to measure the similarity between two data points by their subdimensions. For example, assume that x1, x2, and x3 are ten-dimensional data vectors. The data point x3 is said to be closer to x1 than x2 if more than half of the dimensions of x1 and x3 are closer to x1 than x2. Thus, if two patterns are very similar except for a small amount of features, this measure will preserve the similarity. We have performed eight experiments to test the robustness of the proposed method, including three synthetic data sets, three real world data sets, and two microarray data sets. We also have compared the proposed method with four different clustering algorithms. Experimental results show that the proposed method yields better results than existing clustering algorithms.

Algorithms↗
PubMed2002

Genesis: cluster analysis of microarray data.

A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.

Algorithms↗