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At least 433 records · Page 24Linked to original sources
PubMed1998

[Self-organization neural network based ultrasonic heart image segmentation].

Segmentation is one of the most difficult problems in multidimensional reconstruction of ultrasonic heart image. In the present paper, a segmentation method of ultrasonic heart image using the self-organization neural network has been studied. This is an unsupervised segmentation method, which can segment images through clustering automatically. The results show that the present method has significant benefits over the traditional K-means algorithm.

Algorithms↗
PubMed2003

Overcoming confounded controls in the analysis of gene expression data from microarray experiments.

A potential limitation of data from microarray experiments exists when improper control samples are used. In cancer research, comparisons of tumour expression profiles to those from normal samples is challenging due to tissue heterogeneity (mixed cell populations). A specific example exists in a published colon cancer dataset, in which tissue heterogeneity was reported among the normal samples. In this paper, we show how to overcome or avoid the problem of using normal samples that do not derive from the same tissue of origin as the tumour. We advocate an exploratory unsupervised bootstrap analysis that can reveal unexpected and undesired, but strongly supported, clusters of samples that reflect tissue differences instead of tumour versus normal differences. All of the algorithms used in the analysis, including the maximum difference subset algorithm, unsupervised bootstrap analysis, pooled variance t-test for finding differentially expressed genes and the jackknife to reduce false positives, are incorporated into our online Gene Expression Data Analyzer ( http:// bioinformatics.upmc.edu/GE2/GEDA.html ).

Algorithms↗
PubMed2004

Kernel-based self-organized maps trained with supervised bias for gene expression data analysis.

Self-Organized Maps (SOMs) are a popular approach for analyzing genome-wide expression data. However, most SOM based approaches ignore prior knowledge about functional gene categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known functional characteristics of genes. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.

Algorithms↗
PubMed2005

Using supervised fuzzy clustering to predict protein structural classes.

Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.

Algorithms↗
PubMed2002

Gene expression data analysis with a dynamically extended self-organized map that exploits class information.

MOTIVATION: Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling. RESULTS: The paper adapts a novel Self-Organizing map called supervised Network Self-Organized Map (sNet-SOM) to the peculiarities of multi-labeled gene expression data. The sNet-SOM determines adaptively the number of clusters with a dynamic extension process. This process is driven by an inhomogeneous measure that tries to balance unsupervised, supervised and model complexity criteria. Nodes within a rectangular grid are grown at the boundary nodes, weights rippled from the internal nodes towards the outer nodes of the grid, and whole columns inserted within the map The appropriate level of expansion is determined automatically. Multiple sNet-SOM models are constructed dynamically each for a different unsupervised/supervised balance and model selection criteria are used to select the one optimum one. The results indicate that sNet-SOM yields competitive performance to other recently proposed approaches for supervised classification at a significantly reduced computational cost and it provides extensive exploratory analysis potentiality within the analysis framework. Furthermore, it explores simple design decisions that are easier to comprehend and computationally efficient.

Algorithms↗
PubMed2003

Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling.

PURPOSE: To develop a genome-based classification scheme for clear-cell sarcoma (CCS), also known as melanoma of soft parts (MSP), which would have implications for diagnosis and treatment. This tumor displays characteristic features of soft tissue sarcoma (STS), including deep soft tissue primary location and a characteristic translocation, t(12;22)(q13;q12), involving EWS and ATF1 genes. CCS/MSP also has typical melanoma features, including immunoreactivity for S100 and HMB45, pigmentation, MITF-M expression, and a propensity for regional lymph node metastases. MATERIALS AND METHODS: RNA samples from 21 cell lines and 60 pathologically confirmed cases of STS, melanoma, and CCS/MSP were examined using the U95A GeneChip (Affymetrix, Santa Clara, CA). Hierarchical cluster analysis, principal component analysis, and support vector machine (SVM) analysis exploited genomic correlations within the data to classify CCS/MSP. RESULTS: Unsupervised analyses demonstrated a clear distinction between STS and melanoma and, furthermore, showed that CCS/MSP cluster with the melanomas as a distinct group. A supervised SVM learning approach further validated this finding and provided a user-independent approach to diagnosis. Genes of interest that discriminate CCS/MSP included those encoding melanocyte differentiation antigens, MITF, SOX10, ERBB3, and FGFR1. CONCLUSION: Gene expression profiles support the classification of CCS/MSP as a distinct genomic subtype of melanoma. Analysis of these gene profiles using the SVM may be an important diagnostic tool. Genomic analysis identified potential targets for the development of therapeutic strategies in the treatment of this disease.

Algorithms↗
PubMed2003

Seven clusters in genomic triplet distributions.

In several recent papers new gene-detection algorithms were proposed for detecting protein-coding regions without requiring a learning dataset of already known genes. The fact that unsupervised gene-detection is possible is closely connected to the existence of a cluster structure in oligomer frequency distributions. In this paper we study the cluster structure of several genomes in the space of their triplet frequencies, using a pure data exploration strategy. Several complete genomic sequences were analyzed, using the visualization of tables of triplet frequencies in a sliding window. The distribution of 64-dimensional vectors of triplet frequencies displays a well-detectable cluster structure. The structure was found to consist of seven clusters, corresponding to protein-coding information in three possible phases in one of the two complementary strands and in the non-coding regions with high accuracy (higher than 90% on nucleotide level). Visualizing and understanding the structure allows to analyze effectively the performance of different gene-prediction tools. Since the method does not require extraction of ORFs, it can be applied even for unassembled genomes.

Algorithms↗
PubMed2006

Chemometric tools for classification and elucidation of protein secondary structure from infrared and circular dichroism spectroscopic measurements.

Protein classification and characterization often rely on the information contained in the protein secondary structure. Protein class assignment is usually based on X-ray diffraction measurements, which need the protein in a crystallized form, or on NMR spectra, to obtain the structure of a protein in solution. Simple spectroscopic techniques, such as circular dichroism (CD) and infrared (IR) spectroscopies, are also known to be related to protein secondary structure, but they have seldom been used for protein classification. To see the potential of CD, IR, and combined CD/IR measurements for protein classification, unsupervised pattern recognition methods, Principal Component Analysis (PCA) and cluster analysis, are proposed first to check for natural grouping tendencies of proteins according to their measured spectra. Partial Least Squares Discriminant Analysis (PLS-DA), a supervised pattern recognition method, is used afterwards to test the possibility to model explicitly each protein class and to test these models in class assignment of unknown proteins. Determination of the protein secondary structure, understood as the prediction of the abundance of the different secondary structure motifs in the biomolecule, was carried out with the local regression method interval Partial Least Squares (iPLS). CD, IR, and CD/IR measurements were correlated to the fraction of the motif to be predicted, determined from X-ray measurements. iPLS builds models extracting the spectral information most correlated to a specific secondary motif and avoids the use of irrelevant spectral regions. Spectral intervals chosen by iPLS models provide structural information which can be used to confirm previous biochemical assignments or identify new motif-related spectral features. The predictive ability of the models built with the selected spectral regions has a quality similar to previous classical approaches.

Circular Dichroism↗
PubMed2006

Exploration of global gene expression in human liver steatosis by high-density oligonucleotide microarray.

Understanding the molecular mechanisms underlying fatty liver disease (FLD) in humans is of major importance. We used high-density oligonucleotide microarrays (22.3 K) to assess the mechanisms responsible for the development of human liver steatosis. We compared global gene expression in normal (n=9) and steatotic (n=9) livers without histological signs of inflammation or fibrosis. A total of 34 additional human samples including normal (n=11), steatosis (n=11), HCV-related steatosis (n=4) or steatohepatitis associated with alcohol consumption (n=4) or obesity (n=4) were used for immunohistochemistry or quantitative real-time PCR studies. With unsupervised classification (no gene selection), all steatotic liver samples clustered together. Using step-down maxT multiple testing procedure for controlling the Family-Wise Error-Rate at level 5%, 110 cDNAs (100 over- and 10 underexpressed) were found to be differentially expressed in steatotic and normal livers. Of them were genes involved in mitochondrial phosphorylative and oxidative metabolism. The mean ratio of mitochondrial DNA to nuclear DNA content was higher in liver steatosis compared to normal liver biopsies (1.12+/-0.14 vs 0.67+/-0.10; P=0.01). An increased expression of genes involved in inflammation (IL-1R family, TGFB) was also observed and confirmed by quantitative RT-PCR or immunochemistry. In steatohepatitis, an increase of the protein expression of mitochondrial antigens, IL-1R1, IGF2 and TGFB1 was also observed, interleukin 1 receptor being always strongly expressed in steatohepatitis linked to alcohol or obesity. In conclusion, mitochondrial alterations play a major role in the development of steatosis per se. Activation of inflammatory pathways is present at a very early stage of steatosis, even if no morphological sign of inflammation is observed.

DNA, Mitochondrial↗
PubMed2006

A cost-function approach to rival penalized competitive learning (RPCL).

Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach. By theoretical analysis, we prove that a general form of RPCL, called distance-sensitive RPCL (DSRPCL), is associated with the minimization of a cost function on the weight vectors of a competitive learning network. As a DSRPCL process decreases the cost to a local minimum, a number of weight vectors eventually fall into a hypersphere surrounding the sample data, while the other weight vectors diverge to infinity. Moreover, it is shown by the theoretical analysis and simulation experiments that if the cost reduces into the global minimum, a correct number of weight vectors is automatically selected and located around the centers of the actual clusters, respectively. Finally, we apply the DSRPCL algorithms to unsupervised color image segmentation and classification of the wine data.

Algorithms↗
PubMed2005

Classifying antibodies using flow cytometry data: class prediction and class discovery.

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.

Antibodies, Monoclonal↗
PubMed2005

ProtoNet 4.0: a hierarchical classification of one million protein sequences.

ProtoNet is an automatic hierarchical classification of the protein sequence space. In 2004, the ProtoNet (version 4.0) presents the analysis of over one million proteins merged from SwissProt and TrEMBL databases. In addition to rich visualization and analysis tools to navigate the clustering hierarchy, we incorporated several improvements that allow a simplified view of the scaffold of the proteins. An unsupervised, biologically valid method that was developed resulted in a condensation of the ProtoNet hierarchy to only 12% of the clusters. A large portion of these clusters was automatically assigned high confidence biological names according to their correspondence with functional annotations. ProtoNet is available at: http://www.protonet.cs.huji.ac.il.

Animals↗
PubMed2004

INCA: synonymous codon usage analysis and clustering by means of self-organizing map.

UNLABELLED: INteractive Codon usage Analysis (INCA) provides an array of features useful in analysis of synonymous codon usage in whole genomes. In addition to computing codon frequencies and several usage indices, such as 'codon bias', effective Nc and CAI, the primary strength of INCA has numerous options for the interactive graphical display of calculated values, thus allowing visual detection of various trends in codon usage. Finally, INCA includes a specific unsupervised neural network algorithm, the self-organizing map, used for gene clustering according to the preferred utilization of codons. AVAILABILITY: INCA is available for the Win32 platform and is free of charge for academic use. For details, visit the web page http://www.bioinfo-hr.org/inca or contact the author directly. SUPPLEMENTARY INFORMATION: Software is accompanied with a user manual and a short tutorial.

Algorithms↗
PubMed1976

The influence of supervision and facilitative conditions on the psychotherapeutic effectiveness of nonprofessional and professional therapists.

Sixty poorly adjusted, fifth- and sixth-grade males were randomly assigned to experimental groups seen by professional, supervised nonprofessional, and unsupervised nonprofessional therapists, as well as to a no-treatment control. The therapists saw their clients 10 times. Excerpts from the 3rd and 7th sessions were rated by trained raters for facilitative conditions. Change in clients was studied through instruments which assessed change from three vantage points--the child's, his teacher's and his therapist's. Individual as well as group data demonstrated significant superiority of supervised nonprofessionals over unsupervised nonprofessionals. Changes in clients treated by professionals tended to cluster close to those treated by supervised nonprofessionals. Several significantly negative correlations between facilitative conditions and criteria of psychotherapeutic change were discovered.

Allied Health Personnel↗
PubMed1996

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Algorithms↗
PubMed2006

Molecular profiling reveals myeloid leukemia cell lines to be faithful model systems characterized by distinct genomic aberrations.

To model and investigate different facets of leukemia pathogenesis, a widely accepted approach is to use immortalized leukemia cell lines. Although these provide powerful tools to our knowledge, few studies have addressed the question whether hematopoietic cell lines represent accurate and reliable model systems. To improve the molecular characterization of these model systems, we analyzed 17 myeloid leukemia cell lines using DNA microarray technology. By array-based comparative genomic hybridization, we identified recurrent genomic DNA gains and losses, as well as high-level amplifications. Parallel analysis of gene expression helped delineate potential candidate genes, and unsupervised analysis of gene expression data revealed cell lines to cluster in part based on underlying cytogenetic abnormalities. Comparison with clinical leukemia specimens showed that key signatures were retained, as myeloid cell lines with characteristic cytogenetic aberrations co-clustered with leukemia samples carrying the respective abnormality. Signatures were also quite robust, as expression data from cell lines correlated highly with published data. Thus, our analyses demonstrate myeloid cell lines to exhibit conserved and stable signatures reflecting the underlying primary cytogenetic aberrations. Our refined molecular characterization of myeloid cell lines supports the utility of cell lines as faithful and powerful model systems and provides additional insights into the molecular mechanisms of leukemogenesis.

Acute Disease↗
PubMed2000

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.

BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

Algorithms↗
PubMed2000

[Study on unsupervised classification in marshland of endemic area of Schistosomiasis Japonica using satellite TM image data].

OBJECTIVE: To categorize the vegetation of marshland in the endemic areas of Schistosomiasis Japonica using satellite thematic mapper (TM)data in Liupo Village, Anhui Province. METHODS: Data of three satellite TM hands, namely TM3, TM4 and TM5, were selected to be used in classification with principal component analysis. Then, satellite TM images were applied to false color composite and unsupervised classification with computer software to manage the images. Finally, the resulting clusters were identified and reclassified by site visits to categorize the land coverage and vegetation. RESULTS: Five classifications of land coverage were generated, including poplar forest, bulrush and marshland, all with snail habitants. Based on the survey in the spring of 1998, the overall live snail density and infested snail density in the study areas were 0.510/0.11 m2 and 0.007/0.11 m2, respectively. CONCLUSION: Unsupervised classification, which is a technique for the interpretation of remotely sensed imagery, can contribute to the classification of environment with snail habitants and ecological diseases and become a new method in epidemiological studies.

Animals↗