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PubMedDate not supplied

Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms.

The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG. The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were displayed in a class probability histogram. This histogram reflected in all patients the depth of anaesthesia, when the concentration of the anaesthetic agent was adjusted either based on clinical signs or according to the protocol. This uniform display, where colours in a class probability histogram indicate the depth of anaesthesia, may in the future serve as on-line advice for the administration of anaesthetics. A comparison of multiparametric with single parametric methods, based on calculation of median, spectral edge and peak frequencies, questions the reliability of the single parametric methods in monitoring anaesthetic depth.

Adult↗
PubMedDate not supplied

Review of MR image segmentation techniques using pattern recognition.

This paper has reviewed, with somewhat variable coverage, the nine MR image segmentation techniques itemized in Table II. A wide array of approaches have been discussed; each has its merits and drawbacks. We have also given pointers to other approaches not discussed in depth in this review. The methods reviewed fall roughly into four model groups: c-means, maximum likelihood, neural networks, and k-nearest neighbor rules. Both supervised and unsupervised schemes require human intervention to obtain clinically useful results in MR segmentation. Unsupervised techniques require somewhat less interaction on a per patient/image basis. Maximum likelihood techniques have had some success, but are very susceptible to the choice of training region, which may need to be chosen slice by slice for even one patient. Generally, techniques that must assume an underlying statistical distribution of the data (such as LML and UML) do not appear promising, since tissue regions of interest do not usually obey the distributional tendencies of probability density functions. The most promising supervised techniques reviewed seem to be FF/NN methods that allow hidden layers to be configured as examples are presented to the system. An example of a self-configuring network, FF/CC, was also discussed. The relatively simple k-nearest neighbor rule algorithms (hard and fuzzy) have also shown promise in the supervised category. Unsupervised techniques based upon fuzzy c-means clustering algorithms have also shown great promise in MR image segmentation. Several unsupervised connectionist techniques have recently been experimented with on MR images of the brain and have provided promising initial results. A pixel-intensity-based edge detection algorithm has recently been used to provide promising segmentations of the brain. This is also an unsupervised technique, older versions of which have been susceptible to oversegmenting the image because of the lack of clear boundaries between tissue types or finding uninteresting boundaries between slightly different types of the same tissue. To conclude, we offer some remarks about improving MR segmentation techniques. The better unsupervised techniques are too slow. Improving speed via parallelization and optimization will improve their competitiveness with, e.g., the k-nn rule, which is the fastest technique covered in this review. Another area for development is dynamic cluster validity. Unsupervised methods need better ways to specify and adjust c, the number of tissue classes found by the algorithm. Initialization is a third important area of research. Many of the schemes listed in Table II are sensitive to good initialization, both in terms of the parameters of the design, as well as operator selection of training data.(ABSTRACT TRUNCATED AT 400 WORDS)

Algorithms↗
PubMed1985

Classification of anaemia on the basis of ferrokinetic parameters.

Quantitative information on abnormalities of erythropoiesis and mechanisms of anaemia has been obtained in 136 anaemic patients by means of ferrokinetic studies. To derive a functional classification of anaemia based on ferrokinetic parameters, agglomerative hierarchical cluster analysis and principal coordinate analysis were utilized as techniques for unsupervised classification. Two main clusters were found and named anaemia with low potential erythropoiesis and with high potential erythropoiesis, since the most discriminant parameter between them was total erythroid iron turnover, a measure of total erythropoietic activity. A value of total erythropoiesis equal to 4 times the normal was found to discriminate these two types of anaemia in 94% of cases. Within the group with low potential erythropoiesis, three clusters showing different qualitative disturbances of erythropoiesis were singled out. Among patients with high potential erythropoiesis, two clusters were found. A value of effective erythropoiesis equal to 2.5 times the normal was shown to have a high discriminant power between these clusters. This threshold level distinguished between patients having ineffective erythropoiesis or peripheral haemolysis as the major mechanism of anaemia. The present functional classification of anaemia provides a complete picture of the different pathogenetic mechanisms and may represent the basis for a more rational diagnostic approach to erythroid disorders.

Adolescent↗
PubMed2006

Classification of lactic acid bacteria with UV-resonance Raman spectroscopy.

UV-resonance Raman spectroscopy is applied as a method for the identification of lactic acid bacteria from yogurt. Eight different strains of bacteria from Lactobacillus acidophilus, L. delbrueckii ssp. bulgaricus, and Streptococcus thermophilus were investigated. At an excitation wavelength of 244 nm signals from nucleic acids and proteins are selectively enhanced. Classification was accomplished using different chemometric methods. In a first attempt, the unsupervised methods hierarchical cluster analysis and principal component analysis were applied to investigate natural grouping in the data. In a second step the spectra were analyzed using several supervised methods: K-nearest neighbor classifier, nearest mean classifier, linear discriminant analysis, and support vector machines.

Cluster Analysis↗
PubMed1996

Adaptive computational models of fast learning of motion direction discrimination.

In a previous study, we found that subjects' performance in a task of direction discrimination in stochastic motion stimuli shows fast improvement in the absence of feedback and the learned ability is retained over a period of time. We model this learning using two unsupervised approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Extensive simulations with the models show performance similar to psychophysical results.

Animals↗
PubMed2005

Gene selection for classification of cancers using probabilistic model building genetic algorithm.

Recently, DNA microarray-based gene expression profiles have been used to correlate the clinical behavior of cancers with the differential gene expression levels in cancerous and normal tissues. To this end, after selection of some predictive genes based on signal-to-noise (S2N) ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (k NN) classifier are widely used. Instead of S2N ratio, adaptive searches like Probabilistic Model Building Genetic Algorithm (PMBGA) can be applied for selection of a smaller size gene subset that would classify patient samples more accurately. In this paper, we propose a new PMBGA-based method for identification of informative genes from microarray data. By applying our proposed method to classification of three microarray data sets of binary and multi-type tumors, we demonstrate that the gene subsets selected with our technique yield better classification accuracy.

Algorithms↗
PubMed2004

A DNA methylation pattern similar to normal tissue is associated with better prognosis in human cervical cancer.

Promoter hypermethylation has been recognized to play an important role in carcinogenesis. We analyzed the methylation status of 25 genes in 14 normal cervical tissue specimens and in 65 tissue specimens from cervical cancer patients using the MethyLight technique. Most of the analyzed genes have been shown to be methylated in various cancers. RB1 was never methylated in any analyzed cervical tissue. DNA methylation status of the remaining 24 genes in every tissue sample was subsequently analyzed using unsupervised agglomerative hierarchical cluster analysis to group specimens and CpG regions. We observed four clusters. All normal cervical tissue specimens were grouped together in one cluster. Neither grade, nor histological type nor age demonstrated any significant association with clusters formed. Interestingly, statistically significantly less patients whose tumor DNA methylation pattern clustered together with normal cervical tissue died within our observation period, as compared to those patients out of the three remaining clusters (P < 0.03) Cervical cancer patients, whose DNA methylation pattern clustered together with normal cervical tissue revealed a strong trend to better survival (P = 0.066) compared to patients grouped in the remaining cluster. This study shows for the first time that working solely with DNA methylation pattern a subgroup of cervical cancer patients can be defined that demonstrated strong similarity to non-neoplastic probands and had a better prognosis.

Adult↗
PubMed2003

GEPAS: A web-based resource for microarray gene expression data analysis.

We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).

Cluster Analysis↗
PubMed2005

Extension neural network-type 2 and its applications.

A supervised learning pattern classifier, called the extension neural network (ENN), has been described in a recent paper. In this sequel, the unsupervised learning pattern clustering sibling called the extension neural network type 2 (ENN-2) is proposed. This new neural network uses an extension distance (ED) to measure the similarity between data and the cluster center. It does not require an initial guess of the cluster center coordinates, nor of the initial number of clusters. The clustering process is controlled by a distanced parameter and by a novel extension distance. It shows the same capability as human memory systems to keep stability and plasticity characteristics at the same time, and it can produce meaningful weights after learning. Moreover, the structure of the proposed ENN-2 is simpler and the learning time is shorter than traditional neural networks. Experimental results from five different examples, including three benchmark data sets and two practical applications, verify the effectiveness and applicability of the proposed work.

Algorithms↗
PubMed2005

Identification of biomarkers for breast cancer in nipple aspiration and ductal lavage fluid.

PURPOSE: To establish a comprehensive proteomic approach for biomarker discovery and validation in breast fluid. EXPERIMENTAL DESIGN: A total of 95 specimens from three institutions were used including 10 nipple aspiration fluid (5 stage I/II cancerous breasts and 5 age-matched healthy controls), 42 ductal lavage fluid from 14 patients with unilateral stage I/II cancer (25 from 9 cancerous breasts and 17 from 7 contralateral breasts), and 42 ductal lavage fluid from 14 high-risk women (multiple ducts repeated lavage). Differentially expressed protein/peptides were discovered by proteomic analysis of training sample, using ProteinChip arrays and surface-enhanced laser desorption ionization (SELDI) time-of-flight mass spectrometry, and validated on independently collected testing samples. After protein identification, ELISA was done to confirm the SELDI findings. RESULTS: We were able to obtain reproducible protein profiles using minimal amount of protein (1 mug) by applying an optimized chip protocol and SELDI. We were able to select cancer-associated biomarkers despite large individual variability by applying both unsupervised and supervised cluster analysis. Furthermore, we were able to train and test candidate biomarkers on independently collected samples and identified one component of a multimarker panel as human neutrophil peptides 1 to 3. CONCLUSIONS: Breast fluid is a rich source of breast cancer biomarkers. In combination with high-throughput novel proteomic profiling technology and multicenter study design, markers that are highly specific to breast cancer can be discovered and validated. Our observations also suggest that persistent elevation of human neutrophil peptide in high-risk women may imply early onset of cancer not yet detectable by current detection method. Proof of this hypothesis requires follow-up on a larger study population.

Adenocarcinoma↗
PubMed2006

HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics.

BACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. RESULTS: We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/. CONCLUSION: The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis.

Antigens, CD34↗
PubMed2006

Effects of storage time and exogenous protease inhibitors on plasma protein levels.

Plasma biomarker analysis requires intact unbiased starting material. We analyzed the effects on plasma protein profiles of protease inhibitor cocktails and preprocessing storage. Plasma from 12 healthy subjects collected with and without protease inhibitors was prepared immediately and after 2 hours of room temperature storage. The samples were analyzed by a multiplexed enzyme-linked immunosorbent assay that captured 99 chemokines and cytokines. Unsupervised hierarchical analysis clustered the samples into 4 groups; one composed predominantly of samples processed immediately, another of samples processed after 2 hours, and the remaining two were a mix of immediate and 2-hour samples. The mixed and immediate group protein profiles were similar. However, among the immediate and 2-hour samples, the levels of 37 factors differed significantly: all were greater after 2 hours. The dramatic changes in protein levels during storage were independent of protease inhibitors and are likely due to cytokine production and/or release by leukocytes and platelets.

Blood Preservation↗
PubMed2006

On weighting clustering.

Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with powerful supervised classification methods known as boosting algorithms. However, interest in this analogy has so far been mainly borne out from experimental studies only. This paper is, to the best of our knowledge, the first attempt at its formalization. More precisely, we handle clustering as a constrained minimization of a Bregman divergence. Weight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (weighted) versions of clustering algorithms such as k-means, fuzzy c-means, Expectation Maximization (EM), and k-harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reweighting may bring to clustering.

Algorithms↗
PubMed2004

Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.

Action Potentials↗
PubMed2004

Simultaneous feature selection and clustering using mixture models.

Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.

Algorithms↗
PubMed2006

Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement >or=50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R(2) = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method.

Algorithms↗
PubMed2003

Kernel hierarchical gene clustering from microarray expression data.

MOTIVATION: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. RESULTS: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality. AVAILABILITY: Supplementary data at www.cs.columbia.edu/compbio/hiclust. Software source code available by request.

Algorithms↗
PubMed2004

Incidental carcinoma of the prostate: clinicopathological, stereological and immunohistochemical findings studied with logistic regression and self-organizing feature maps.

OBJECTIVE: To identify significant predictive factors determining category T1a and T1b in incidental prostatic carcinoma with classical and neural multivariate data analysis methods. MATERIALS AND METHODS: Incidental prostatic carcinomas diagnosed in our department during 1990-99 (66 cases) were re-examined. Besides acquiring routine clinical and pathological data the tumours were assessed by scoring immunohistochemistry for proliferative activity and p53-overexpression. Tumour vascularization (angiogenesis) and epithelial texture variables were investigated by quantitative stereology. The data were evaluated by classical statistical methods (t-test, correlation analysis, logistic regression). Moreover, self-organizing feature maps (SOMs) were applied as an exploratory approach to unsupervised data analysis by artificial neural networks. RESULTS: The proliferative fraction, p53 overexpression of tumour cell nuclei, preoperative prostate-specific antigen value and density of capillary vascularization correlated with the Gleason score in incidental prostatic carcinoma. In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, the Gleason score and the volume fraction of epithelial cells were significant independent predictors of the tumour category. The cases could be grouped into clusters of different degrees of malignancy using SOMs. CONCLUSIONS: Texture variables of tumour cells are of central importance for the extent of propagation in the prostate in incidental prostatic adenocarcinomas. Gleason score and quantitative stereological estimates of the volume fraction of tumour cells are significant predictors of T1a and T1b categories of incidental prostatic carcinoma. Unsupervised clustering of T1 prostate carcinoma cases by SOMs correlates well with the dichotomous classification into T1a and T1b according to the UICC.

Aged↗