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At least 721 records · Page 40Linked to original sources
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↗
PubMed1995

A hybrid-hybrid matrix method for 3D NOE-NOE data analysis.

A hybrid-hybrid matrix method is described that quantitatively analyzes 3D NOE-NOE NMR data. Experimental 3D data are merged with simulated 3D data to create a hybrid 3D NOE-NOE spectrum. This is then deconvoluted into a 2D hybrid NOESY spectrum. The deconvoluted, 2D hybrid NOESY spectrum can then be merged with other 2D NOESY experimental data along with additional simulated 2D data as necessary to create a hybrid-hybrid 2D NOE volume matrix. This hybrid-hybrid volume matrix is then used with the complete relaxation program, MORASS, to calculate a rate matrix, and the resulting distances taken from the off-diagonal cross-relaxation rates can then be utilized in a distance geometry or restrained molecular-dynamics refinement of the structure. This process is repeated until a satisfactory agreement between the calculated and observed 3D volumes is obtained. This hybrid-hybrid matrix method retains computational efficiency and utilizes the resolution of the 3D data set while retaining any information content of the available 2D data. The initial tests of the deconvolution algorithm give high correlation results even with the introduction of random error into the 3D data set. Our results suggest that the hybrid-hybrid matrix method for analysis of 3D NOE-NOE spectra may provide a viable tool in the refinement of large molecules.

Algorithms↗
PubMed2004

Multiple neural spike train data analysis: state-of-the-art and future challenges.

Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.

Action Potentials↗
PubMed2001

Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data.

Upon the completion of the SACCHAROMYCES: cerevisiae genomic sequence in 1996 [Goffeau,A. et al. (1997) NATURE:, 387, 5], several creative and ambitious projects have been initiated to explore the functions of gene products or gene expression on a genome-wide scale. To help researchers take advantage of these projects, the SACCHAROMYCES: Genome Database (SGD) has created two new tools, Function Junction and Expression Connection. Together, the tools form a central resource for querying multiple large-scale analysis projects for data about individual genes. Function Junction provides information from diverse projects that shed light on the role a gene product plays in the cell, while Expression Connection delivers information produced by the ever-increasing number of microarray projects. WWW access to SGD is available at genome-www.stanford. edu/Saccharomyces/.

Databases, Factual↗
PubMedDate not supplied

[Data analysis of daily reports in general medicine].

Health priorities in a sample of 1,425 subjects of a defined urban population were studied from data collected routinely in general practice. Analysis of prevalence has shown 1/5 of circulatory diseases, 1/7 of respiratory diseases, 1/8 of musculoskeletal diseases, and 1/10 of mental disorders. According to incidence for the first medical examination, the leading diagnostic categories were diseases of the respiratory, circulatory, musculoskeletal, digestive, and genitourinary system. Both prevalence and incidence have shown that health priorities in the studied population were diseases of circulatory system, with hypertension, and diseases of the respiratory system, with common cold, of the highest prevalence.

Adolescent↗
PubMedDate not supplied

Problems of test choice and data analysis in behavioral teratology: the case of prenatal benzodiazepines.

Higher-tier tests for the assessment of early treatment effects should be aimed at providing specific information on the behavior processes affected, rather than simply at extending the descriptive data base. The contrast between positive and negative results can be useful to point out possible mechanisms of action. For example, late prenatal oxazepam exposure of mice produced a reduction of the amphetamine hyperactivity at the end of the second postnatal week, but did not significantly affect the response to scopolamine at the end of the third week. An impairment of active locomotor avoidance was observed at the young adult stage, which contrasted with the absence or scarcity of changes in passive avoidance and extinction responding in the same go-no go tests. These changes in response-activating mechanisms appear to be in agreement with the medium- and long-term effects on CNS monoamine metabolism described in the literature. As concerns statistical analysis, dichotomous or polytomous data obtained, e.g., by the Fox battery are not yet amenable to an adequate processing, due to the shortcomings of the available nonparametric tests. By contrast, mixed-model ANOVAs can cope with complex data obtained, e.g., in activity and learning tests. However, the available checks on various assumptions (normality, homogeneity of variance, sphericity) are not valid when nested factors, block factors and repeated measures coexist. Finally, the more usual cross-fostering procedures provide adequate information on some aspects (e.g., separation of main effects of prenatal treatments from postnatal maternal effects) but not on others.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMedDate not supplied

Using in vitro prediction models instead of the rabbit eye irritation test to classify and label new chemicals: a post hoc data analysis of the international EC/HO validation study.

The international validation study on alternative methods to replace the Draize rabbit eye irritation test, funded by the European Commission (EC) and the British Home Office (HO), took place during 1992-1994, and the results were published in 1995. The results of this EC/HO study are analysed by employing discriminant analysis, taking into account the classification of the in vivo data into eye irritation classes A (risk of serious damage to eyes), B (irritating to eyes) and NI (non-irritant). A data set for 59 test items was analysed, together with three subsets: surfactants, water-soluble chemicals, and water-insoluble chemicals. The new statistical methods of feature selection and estimation of the discriminant functions classification error were used. Normal distributed random numbers were added to the mean values of each in vitro endpoint, depending on the observed standard deviations. Thereafter, the reclassification error of the random observations was estimated by applying the fixed function of the mean values. Moreover, the leaving-one-out cross-classification method was applied to this random data set. Subsequently, random data were generated r times (for example, r = 1000) for a feature combination. Eighteen features were investigated in nine in vitro test systems to predict the effects of a chemical in the rabbit eye. 72.5% of the chemicals in the undivided sample were correctly classified when applying the in vitro endpoints lgNRU of the neutral red uptake test and lgBCOPo5 of the bovine opacity and permeability test. The accuracy increased to 80.9% when six in vitro features were used, and the sample was subdivided. The subset of surfactants was correctly classified in more than 90% of cases, which is an excellent performance.

Animal Testing Alternatives↗
PubMed2001

Nonadditive two-way ANOVA for event-related fMRI data analysis.

A significant recent development in functional magnetic resonance imaging (fMRI) is the introduction of event-related fMRI, also known as time-resolved fMRI. Because the exact shape of the MR response in an event-related fMRI experiment is often not known, traditional methods developed for block design experiments, such as t test and correlation analysis, are not well-suited for extracting activated pixels from the event-related data. In this work, a statistical technique based on nonadditive two-way analysis of variance is developed for use in event-related studies. Theoretical and experimental work were carried out for establishing a statistical threshold to determine pixel activation. Experimental studies were performed to demonstrate the utility of this approach.

Adult↗
PubMed2003

Screening test data analysis for liver disease prediction model using growth curve.

This study was done based on screening test data accumulated from 1994 to 2001 for studying of risk factor related with liver disease and prediction model of liver disease. In the existing study related with liver, the main current is studying on liver cancer, not on liver disease, previous step into liver cancer. As a result of estimating prediction model through the risk factors of liver disease and the growth curve on the basis of data, it is shown that most of the risk factors about liver disease are also those about known well as liver cancer. In addition, to investigate liver disease prevalence from the viewpoint of the future, this study presumed risk factor through the various growth curve analysis and examined logistic regression, decision tree and neural network from those estimators. In the case of neural network using growth curve estimator of Xi(5)=alphai+betaiT+epsiloniT, accuracy of liver disease was 72.55% and sensitivity was 78.62%. On the other hand, in the case of liver disease prediction model using recent screening test data estimator, accuracy was 72.09% and sensitivity was 71.72%. Those are lower than liver disease prediction model of growth curve analysis. In the various liver disease prediction models assumed by growth curve and many distinction models, when growth curve estimator was used, sensitivity value was improved.

Humans↗
PubMed2003

A statistical perspective on gene expression data analysis.

Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods.

Cluster Analysis↗
PubMed1998

Qualitative methods in arthritis research: sampling and data analysis.

Qualitative research presents unique opportunities for understanding arthritis from the perspective of those affected by the condition, as well as for critically evaluating many of the associations of traditional psychosocial variables that have emerged from decades of quantitative research. Its growing acceptance and popularity in the health sciences is reflected in the number of program announcements and other research requests that stress the need to understand health and disease in the context of human diversity. At the same time, it is important that qualitative research be done well and be critically evaluated in the same manner as quantitative research. Many of the same cautions (e.g., garbage in, garbage out) and principles (e.g., standardization of data collection and analysis steps, documentation of research activities) apply in all types of scientific inquiry. By maintaining high standards for qualitative research, those conducting and evaluating qualitative research can ensure its continued acceptance as a valid and powerful mode of research.

Arthritis↗
PubMed2006

Multiparametric iterative self-organizing data analysis of ischemic lesions using pre- or post-Gd T1 MRI.

BACKGROUND: The purpose of this work was to evaluate effects of Gd-diethylenetriaminepentacetic acid (DTPA) injection on T(1)-weighted images of stroke and lesion segmentation and characterization results generated by our multiparametric iterative self-organizing data (ISODATA) method. The post-Gd image incorporates vasculature information into the analysis. METHODS: Either a pre-Gd T(1)-weighted image (T1WI) or a post-Gd T1WI was used along with diffusion-, T(2)- and proton-density-weighted images in the analysis. ISODATA is a data-driven method that segments and characterizes tissue damage in stroke using multiparametric MRI. RESULTS: Experimental results in both animal and human studies showed that the use of post-Gd T1WI modified the segmentation and characterization results on the periphery of the lesion. The peripheral region that changes with Gd-DTPA has a higher permeability compared to the rest of the lesion. Either of the data sets (including pre- or post-Gd T1WI) was used to estimate the tissue recovery and generated consistent results. CONCLUSIONS: This study shows that our multiparametric ISODATA approach consistently identifies and characterizes the core of the ischemic lesion. It also shows that the inclusion of post-Gd T1WI results in the segmentation and characterization of the lesion periphery if it has a higher permeability compared to the rest of the lesion. Finally, it confirms that the multiparametric ISODATA MRI characterizes tissue damage and recovery in stroke.

Algorithms↗