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PubMed2003

Autonomous system for Web-based microarray image analysis.

Software-based feature extraction from DNA microarray images still requires human intervention on various levels. Manual adjustment of grid and metagrid parameters, precise alignment of superimposed grid templates and gene spots, or simply identification of large-scale artifacts have to be performed beforehand to reliably analyze DNA signals and correctly quantify their expression values. Ideally, a Web-based system with input solely confined to a single microarray image and a data table as output containing measurements for all gene spots would directly transform raw image data into abstracted gene expression tables. Sophisticated algorithms with advanced procedures for iterative correction function can overcome imminent challenges in image processing. Herein is introduced an integrated software system with a Java-based interface on the client side that allows for decentralized access and furthermore enables the scientist to instantly employ the most updated software version at any given time. This software tool is extended from PixClust as used in Extractiff incorporated with Java Web Start deployment technology. Ultimately, this setup is destined for high-throughput pipelines in genome-wide medical diagnostics labs or microarray core facilities aimed at providing fully automated service to its users.

Algorithms↗

PubMed2005

Mixtures-of-experts of autoregressive time series: asymptotic normality and model specification.

We consider a class of nonlinear models based on mixtures of local autoregressive time series. At any given time point, we have a certain number of linear models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, we assume the existence of a latent multinomial variable, whose distribution depends on the same covariates as the experts, that determines which linear process is observed. This structure, denoted as mixture-of-experts (ME), is considerably flexible in modeling the conditional mean function, as shown by Jiang and Tanner. In this paper, we present a formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator (MLE), under stationarity and nonstationarity, and under correct model specification and model misspecification. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, we present applications to simulated and real data sets, to illustrate the ability of the proposed structure to model not only the conditional mean, but also the whole conditional density.

Algorithms↗

PubMed2005

A universal learning rule that minimizes well-formed cost functions.

In this paper, we analyze stochastic gradient learning rules for posterior probability estimation using networks with a single layer of weights and a general nonlinear activation function. We provide necessary and sufficient conditions on the learning rules and the activation function to obtain probability estimates. Also, we extend the concept of well-formed cost function, proposed by Wittner and Denker, to multiclass problems, and we provide theoretical results showing the advantages of this kind of objective functions.

Algorithms↗

PubMed2005

Constructive feedforward neural networks using hermite polynomial activation functions.

In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.

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PubMed2005

Neuron selection for RBF neural network classifier based on data structure preserving criterion.

The central problem in training a radial basis function neural network is the selection of hidden layer neurons. In this paper, we propose to select hidden layer neurons based on data structure preserving criterion. Data structure denotes relative location of samples in the high-dimensional space. By preserving the data structure of samples including those that are close to separation boundaries between different classes, the neuron subset selected retains the separation margin underlying the full set of hidden layer neurons. As a direct result, the network obtained tends to generalize well.

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PubMed2006

Topology description for data distributions using a topology graph with divide-and-combine learning strategy.

The topologies of data distributions are very important for data description. Usually, it is not easy to find a description that can give us an intuitional understanding of the topologies for general distributions. In this paper, a novel concept, a topology graph, is proposed as a description for the principal topology of data distribution. The topology graph builds a one-to-one correspondence between the principal topology of the distribution and the topology itself: annularity features of the principal topology correspond to the loops of the graph, and the divarication features correspond to the branches of the graph. In general, the topology graph can be considered as the skeleton of the data distribution. A divide-and-combine learning strategy is developed to find the topology graphs for general data distributions. The learning strategy is focused on the constrained local description learning and automatic topology generation. Following the learning strategy, a cluster growing algorithm is developed. Experimental results on both artificial datasets and real-world applications show good performance of the proposed algorithm.

Journal Article↗

PubMedDate not supplied

A multiresolution representation for massive meshes.

We present a new external memory multiresolution surface representation for massive polygonal meshes. Previous methods for building such data structures have relied on resampled surface data or employed memory intensive construction algorithms that do not scale well. Our proposed representation combines efficient access to sampled surface data with access to the original surface. The construction algorithm for the surface representation exhibits memory requirements that are insensitive to the size of the input mesh, allowing it to process meshes containing hundreds of millions of polygons. The multiresolution nature of the surface representation has allowed us to develop efficient algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification of massive meshes. The empirical performance of these algorithms demonstrates that the underlying data structure is a powerful and flexible tool for operating on massive geometric data.

Algorithms↗

PubMedDate not supplied

Balancing systematic and flexible exploration of social networks.

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks.

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PubMed2003

Exploring the nonlinear geometry of protein homology.

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies.

Algorithms↗

PubMed2006

A limited universe of membrane protein families and folds.

One of the goals of structural genomics is to obtain a structural representative of almost every fold in nature. A recent estimate suggests that 70%-80% of soluble protein domains identified in the first 1000 genome sequences should be covered by about 25,000 structures-a reasonably achievable goal. As no current estimates exist for the number of membrane protein families, however, it is not possible to know whether family coverage is a realistic goal for membrane proteins. Here we find that virtually all polytopic helical membrane protein families are present in the already known sequences so we can make an estimate of the total number of families. We find that only approximately 700 polytopic membrane protein families account for 80% of structured residues and approximately 1700 cover 90% of structured residues. While apparently a finite and reachable goal, we estimate that it will likely take more than three decades to obtain the structures needed for 90% residue coverage, if current trends continue.

Algorithms↗

PubMed2002

Contact order and ab initio protein structure prediction.

Although much of the motivation for experimental studies of protein folding is to obtain insights for improving protein structure prediction, there has been relatively little connection between experimental protein folding studies and computational structural prediction work in recent years. In the present study, we show that the relationship between protein folding rates and the contact order (CO) of the native structure has implications for ab initio protein structure prediction. Rosetta ab initio folding simulations produce a dearth of high CO structures and an excess of low CO structures, as expected if the computer simulations mimic to some extent the actual folding process. Consistent with this, the majority of failures in ab initio prediction in the CASP4 (critical assessment of structure prediction) experiment involved high CO structures likely to fold much more slowly than the lower CO structures for which reasonable predictions were made. This bias against high CO structures can be partially alleviated by performing large numbers of additional simulations, selecting out the higher CO structures, and eliminating the very low CO structures; this leads to a modest improvement in prediction quality. More significant improvements in predictions for proteins with complex topologies may be possible following significant increases in high-performance computing power, which will be required for thoroughly sampling high CO conformations (high CO proteins can take six orders of magnitude longer to fold than low CO proteins). Importantly for such a strategy, simulations performed for high CO structures converge much less strongly than those for low CO structures, and hence, lack of simulation convergence can indicate the need for improved sampling of high CO conformations. The parallels between Rosetta simulations and folding in vivo may extend to misfolding: The very low CO structures that accumulate in Rosetta simulations consist primarily of local up-down beta-sheets that may resemble precursors to amyloid formation.

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PubMed1990

Secretory dynamics of oestradiol (E2) and progesterone (P4) during periods of relative pituitary LH quiescence in the midluteal phase of the menstrual cycle.

Although the temporal relationship between pulsatile pituitary luteinizing hormone (LH) secretion and steroid hormone release from the corpus luteum has been investigated, the secretory profiles of oestradiol (E2) and progesterone (P4) during periods without any discernible LH pulsatile activity remain unknown. Consequently, blood was sampled at 15-min intervals for 24 h from 16 women during the midluteal phases (6-8 days after midcycle LH surge) of their cycles. LH was measured in all samples and analysed for significant pulses by the Cluster pulse algorithm. Nine studies showing the lowest LH pulse frequencies and large LH pulse amplitudes were also assessed for E2 and P4 in all samples. All three hormones were released in pulsatile fashions. Pulses of E2 and P4 were found to be synchronous. While the release frequencies for E2 (mean +/- SEM: 8.9 +/- 0.7 pulses/24 h) and P4 (8.5 +/- 0.7 pulses/24 h) were comparable, the LH pulse frequency (4.6 +/- 0.4 pulses/24 h) was found to be significantly (P less than 0.001) lower than the ovarian steroid pulse frequencies. Maximum (P less than 0.01) cross-correlation coefficients were determined at positive time lags of 28.1 +/- 7.7 min for LH/E2 and 31.7 +/- 5.8 min for LH/P4, indicating that changes in E2 or P4 levels tended to occur within approximately 30 min following LH concentration changes. Further, the degree of concomitance between a steroid pulse and an LH peak was much higher (P less than 0.001) than by chance. Maximum (P less than 0.01) cross-correlation coefficients between E2 and P4 hormonal data series were found at zero time lag, suggesting that these sex steroids were secreted simultaneously. The pulse amplitudes, pulse durations and areas under the peaks of those E2 or P4 pulses preceded by large (greater than 5 IU/l) amplitude LH pulse were significantly greater (P less than 0.05 or less for all comparisons) than for steroid pulses not associated with preceding LH pulses. Thus, two populations of steroid pulses were observed; one associated with preceding LH pulses and having greater magnitude of all pulse attributes (duration, amplitude, area under the peaks), and another, not associated with preceding LH pulses and having pulse characteristics of lower magnitude. This observation suggests that the pulsatile release of ovarian steroids is a result of the episodic modulating influence of LH and that pulsatile steroid hormone secretion pertains with smaller magnitude during periods of relative pituitary quiescence of LH pulsatility.

Adult↗

PubMed1991

Correlations between mean LH levels and LH pulse characteristics: differences between normal and anovulatory women.

OBJECTIVE: Since LH secretion occurs as a series of pulses, relationships between mean LH levels and LH pulse characteristics are to be expected. The aim of this study was to determine whether such relationships are similar in normal women and anovulatory patients. DESIGN: We studied the correlations between mean LH levels and the products amplitude x frequency and area x frequency of LH pulses in normal women and in patients with disorders of ovulation. Blood samples were taken from each subject every 10 minutes during 6 hours on the 8th day after the last menses. PATIENTS: The patients were divided into three groups: patients with polycystic ovary syndrome (n = 11), patients with idiopathic anovulation (n = 14) and patients with short luteal phase (n = 13). Their results were compared to those of 12 normal women. MEASUREMENTS: LH was evaluated with an immunoradiometric assay. LH data were analysed with a 3 standard deviation threshold criterion for significant peaks, and with cluster analysis algorithm using 1, 2.5 and 5% false positive error rates and 'optimal parameters' (which give less than 5% false positive and false negative error rates in LH male data). RESULTS: Highly significant correlations between amplitude x frequency, area x frequency and mean LH were found in normals and patients with short luteal phase; no significant correlation was found in patients with polycystic ovary syndrome, while significant correlations were found in patients with idiopathic anovulation only with some of our criteria for peak detection. CONCLUSION: The differences that we found between the groups suggest that when commonly used methods are employed to determine LH pulse characteristics, most of the significant LH pulses are taken into account in normals and patients with short luteal phase, but not in anovulatory patients, especially in patients with polycystic ovary syndrome. This method using two correlations appears to be a simple and useful way to show the differences in the mechanisms by which mean LH levels are achieved in normal subjects and patients.

Adult↗

PubMed1994

Serotoninergic control of gonadotrophin and prolactin secretion in women.

OBJECTIVE: Due to conflicting observations from previous investigations, the role of serotonin (5-HT) in the regulation of the human menstrual cycle has not been clearly established. We have therefore investigated the possible participation of 5-HR in the control of gonadotrophin and PRL secretion in women, using the potent 5-HT3 receptor antagonist ondansetron as a pharmacological probe. DESIGN: Serum profiles of LH, FSH and PRL were obtained in 9 normally cycling women during a control and a treatment cycle, during which ondansetron (8 mg orally) was administered daily. On day 10 of both cycles, the serum pulsatility of LH, FSH and PRL was assessed by frequent blood sampling (at 10-minute intervals for 10 hours). Pituitary responsiveness was tested by administration of a GnRH bolus (25 micrograms i.v. after 8 hours). MEASUREMENTS: LH, FSH and PRL were serially determined in all blood samples by immunofluorescence assays. The resulting hormone data arrays were searched for significant fluctuations by the Cluster pulse algorithm. RESULTS: Compared with control cycles, the temporal organization and the endocrine characteristics of the treatment cycles remained virtually unaltered. Serotonin antagonism did not noticeably affect the LH pulse attributes (frequencies, interpulse intervals, amplitudes). Although FSH amplitudes declined markedly (P < 0.05), the remaining pulse attributes were unchanged. A clear increase (P < 0.05) in the PRL pulse frequency was noted, while PRL pulse amplitudes tended to increase (P = 0.1). Gonadotrophin and PRL release in response to GnRH administration was unaltered by ondansetron treatment. CONCLUSIONS: Serotoninergic blockade by a selective 5-HT3 receptor antagonist failed to modify pulsatile LH secretion, but induced distinct changes in episodic FSH and PRL secretion. Since the pituitary gonadotrophin and PRL responsiveness remained unaltered during 5-HT3 receptor blockade, the observed alterations in the FSH and PRL secretion presumably relate to altered hypothalamic regulation of these pituitary hormones. Thus, the central regulation of pulsatile FSH and PRL release in women appears to involve 5-HT3 receptor-mediated processes.

Adult↗

PubMed2005

Colors in atypical nevi: a computer description reproducing clinical assessment.

BACKGROUND/PURPOSE: Atypical nevi (AN) share some dermoscopic features with early melanoma (MM), and computer elaboration of digital images could represent a useful support to diagnosis to assess automatically colors in AN, and to compare the data with those referring to clearly benign nevi (BN) and MMs. METHODS: An image analysis program enabling the numerical description of color areas in melanocytic lesions was used on 459 videomicroscopic images, referring to 76 AN, 288 clearly BN and 95 MMs. RESULTS: Black, white and blue-gray were more frequently found in AN than in clearly BN, but less frequently than in MMs. Color area values significantly differed between the three groups. CONCLUSION: The clinical-morphological interpretation of the numerical data, based on the mathematical description of the aspect and distribution of different color areas in different lesion types may contribute to the characterization of AN and their distinction from MMs.

Algorithms↗

PubMedDate not supplied

Classification of in vivo autofluorescence spectra using support vector machines.

An algorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. Leave-one-out cross-validation was used to evaluate the performance of the algorithms. An overall diagnostic accuracy of 96%, a sensitivity of 94%, and a specificity of 97% for discriminating nasopharyngeal carcinomas from normal tissues were achieved using a linear SVM algorithm. A diagnostic accuracy of 98%, a sensitivity of 95%, and a specificity of 99% for detecting NPC were achieved with a nonlinear SVM algorithm. In a comparison with previously developed algorithms using the same dataset and the principal component analysis (PCA) technique, the SVM algorithms produced better diagnostic accuracy in all instances. In addition, we investigated a method combining PCA and SVM techniques for reducing the complexity of the SVM algorithms.

Algorithms↗

PubMed2003

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees.

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.

Algorithms↗

PubMed2003

Automated lung nodule classification following automated nodule detection on CT: a serial approach.

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.

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