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At least 127 records · Page 7Linked to original sources

Methodology for pharmacokinetic/pharmacodynamic data analysis.

This review will highlight the role of exploratory data analysis and nonlinear regression software in pharmacokinetic-pharmacodynamic (PK/PD) data analysis. Kinetic and dynamic modelling situations typical to the pharmacokineticist in the drug industry will be addressed, and two case studies, including single-dose intravenous bolus plasma data and multiple-dose response-time data will be analysed. Approaches to assessing the suitability of model fit to the observed data will be discussed, and a summary of the key features of available commercial PK software will be given. Specific emphasis will be placed on the application of WinNonlin.

Journal Article↗

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.

Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.

Brain↗

A data analysis microcomputer package (DAMP) for biomedical signals.

The advent of cheap, powerful microcomputer systems makes the analysis of data via sophisticated techniques available to the personnel who are non-specialists in computing systems. The DAMP package described here is intended for use on personal computers and has therefore been written in BASIC for portability. The analysis techniques are powerful, comprising algorithms to perform sample-data generation, plotting displays, digital data filtering, auto-correlation functions, fast Fourier transforms and autoregressive modelling. The last technique contains a number of options including the display of z-plane plots, frequency response of the model, residual plotting and auto-correlation of the residuals. Illustrative results are shown from psychological mood data and rat locomotor activity. The package is designed both to instruct a user in the techniques of spectral analysis, and also to provide a range of methods for investigating time and frequency behaviour of biomedical data.

Biomedical Engineering↗

Waking and sleeping states in the rat from an EEG data analysis point of view.

This article presents the characteristics of ECoGs of arousal, slow wave sleep and paradoxical sleep in the rat, in terms of analysis of data. In a first part, we have applied four different methods of analysis to the three tracings: the instantaneous amplitude histograms computation, the integrative method of Drohocki, the spectral analysis and the normalized slope descriptor method of Hjorth. Each method provides, after data reduction, characteristic parameters of the tracings. A graph which displays peak spectral frequency versus mean integrated value is enough to discriminate between the 3 quantified tracings. Multivariate discriminant analysis reveals that 3 coefficients altogether allow a good discrimination. In the second part we ask the question: which kind of signal is the paradoxical sleep tracing? After the impossibility to choose between a narrow-band Gaussian process or a sinusoidal wave burried in noise, we propose a third kind of signal found after modulation analysis. This signal is modulated both in amplitude and frequency around a carrier frequency beeing the dominant theta rhythm.

Animals↗

Evaluation of four methods of DNA distribution data analysis based on bromodeoxyuridine/DNA bivariate data.

Four published methods of DNA-content histogram analysis (those of Fried, Dean and Jett, simplified Dean, and Fox) were compared using a double labeling of different cell populations. Partially synchronized and asynchronous cell populations were incubated with bromodeoxyuridine (BrdUrd) and then stained with an anti-BrdUrd monoclonal antibody and propidium iodide (PI). The fractions of cells in the G1, S, and G2 + M phases were calculated by each method and compared with those derived from G1, S, and G2 + M areas plotted on BrdUrd/DNA bivariate histograms, taken as the "true" values. This procedure enabled an optimal choice of method for a given cell population.

Antibodies, Monoclonal↗

The relation between baseline HIV drug resistance and response to antiretroviral therapy: re-analysis of retrospective and prospective studies using a standardized data analysis plan.

To assess the relation between resistance to antiretroviral drugs for treatment of HIV-1 infection and virological response to therapy, results from 12 different studies were re-analysed according to a standard data analysis plan. These studies included nine clinical trials and three observational cohorts. The primary end-point in our analyses was virological failure by week 24. Baseline factors that were investigated as predictors of virological failure were plasma HIV-1 RNA, the number and type of new antiretroviral drugs in the regimen, and viral susceptibility to the drugs in the regimen, determined by genotyping or phenotyping methods. These analyses confirmed the importance of both genotypic and phenotypic drug resistance as predictors of virological failure, whether these factors were analysed separately or adjusted for other baseline confounding factors. In most of the re-analysed studies, the odds of virological failure were reduced by about twofold for each additional drug in the regimen to which the patient's virus was sensitive by genotyping methods, and by about two- to threefold for each additional drug that was sensitive by phenotyping.

Anti-HIV Agents↗

Not resting on its laurels, Sentara taps data analysis tool to fine-tune pathways.

Artificial intelligence data analysis leads health care system to fine-tune and improve new clinical pathways. Sentara Health Care in Norfolk, VA, developed a host of new clinical protocols in recent years, but instead of sitting back and enjoying initial improved outcomes and cost savings, it went the extra mile to enhance those pathways using a sophisticated data analysis tool.

Artificial Intelligence↗

Determination of the rate of cerebral oxygen consumption and regional cerebral blood flow by non-invasive 17O in vivo NMR spectroscopy and magnetic resonance imaging: Part 1. Theory and data analysis methods.

Theory and novel data analysis methods of 17O inhalation measurements are presented for the calculation of CMRO2, regional cerebral blood flow (rCBF), the reflow (R), the arterial venous difference (AVD) and the partition coefficient (lambda). Several of the methods proposed for the determination of CMRO2 do not require measurements of regional cerebral blood flow and H2(17)O arterial concentration. All methods of analysis are based on the Kety-Schmidt approach.

Animals↗

Accelerated throughput metabolic route screening in early drug discovery using high-resolution liquid chromatography/quadrupole time-of-flight mass spectrometry and automated data analysis.

The resource investment required to characterise the metabolic fate of a compound is relatively large, meaning that within a drug discovery environment relatively few compounds are characterised in depth. Rate-limiting steps include the setting up of a complex array of mass spectrometry experiments and the subsequent analysis of the large data sets produced. We describe here a strategy for the evaluation of metabolic routes using full-scan high-resolution liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/QToFMS) with automated data analysis using Metabolynx, a commercially available software package. Data from several structurally diverse compounds taken from the literature illustrate that, with careful setting of key parameters, this approach is able to indicate the presence of a wide range of metabolites with only a limited requirement for manual intervention.

Animals↗

[Basic principles of statistical methods applied to public health surveillance data analysis: a review].

Statistical methods in public health surveillance data analysis are important for detecting temporal and/or spatial disease clusters, which can indicate an outbreak or an epidemic. In this paper we present a review and literature survey introducing the public health surveillance process and focusing on some statistical methods which are indicated to the data analysis in routine procedure. Comparative studies of these methods are also analyzed.

Data Interpretation, Statistical↗

Functional data analysis with application to periodically stimulated foetal heart rate data. II: functional logistic regression.

We present a basis solution for the modelling of a binary response with a functional covariate plus any number of scalar covariates. This can be thought of as singular longitudinal data analysis as there are more measurements on the functional covariate than subjects in the study. The maximum likelihood parameter estimates are found using a basis expansion and a modified Fisher scoring algorithm. This technique has been extended to model a functional covariate with a repeated stimulus. We used periodically stimulated foetal heart rate tracings to predict the probability of a high risk birth outcome. It was found that these tracings could predict 94.1 per cent of the high risk pregnancies and without the stimulus, the heart rates were no more predictive than chance.

Adult↗

Precise determination of the dielectric constant and thickness of a nanolayer by use of surface plasmon resonance sensing and multiexperiment linear data analysis.

Surface plasmon resonance (SPR) sensing and an enhanced data analysis technique are used to obtain precise predictions of the dielectric constant and thickness of a nanolayer. In the proposed approach, a modified analytical method is used to obtain initial estimates of the dielectric constants and thicknesses of the metal film and a nanolayer on the sensing surface of a SPR sensor. A multiexperiment data analysis approach based on a two-solvent SPR method is then employed to improve the initial estimates by suppressing the noise in the measurement data. The proposed two-stage approach is employed to determine the dielectric constant and thickness of a molecular imprinting polymer nanolayer. It is found that the results are in good agreement with those obtained with an ellipsometer and a high-resolution scanning electron microscope.

Journal Article↗

Discussion of PET workshop reports, including recommendations of PET Data Analysis Working Group.

On May 1-2, 1989, a PET Data Analysis Working Group convened to consider positron emission tomography (PET) methodology and data analysis. The papers presented and the recommendations of the Group are reviewed. The Group recommended that a standard phantom of the human brain be used by different institutions to examine machine and data reconstruction PET variables. Interinstitutional comparisons could be aided by using a standard three-dimensional coordinate system. Deformations within individual diseased or atypical brains would require nonlinear as well as linear transformations to the standard space, using magnetic resonance images in register with the PET images. Methods for intersubject averaging of pixel-by-pixel or region-of-interest data, as well as appropriate statistical methods, need to be developed. PET data may first be exploratory and hypothesis-generating (with less stringent statistical theory), then later used to test hypotheses (with more stringent statistical criteria). Common databases, obtained by computer simulation models with known inherent structure, or directly by PET measurements on different groups, could be used to compare analytical and statistical methods among institutions.

Brain↗

Functional data analysis of prosodic effects on articulatory timing.

An application of functional data analysis (FDA) (Ramsay and Silverman, 2005, Functional Data Analysis, 2nd ed. (Springer-Verlag, New York)) for linguistic experimentation is explored. The functional time-registration method provided by FDA is shown to offer novel advantages in the investigation of articulatory timing. Traditionally, articulatory studies examining the effects of linguistic variables such as prosody on articulatory timing have relied on comparing the durations of speech intervals of interest defined by kinematic landmarks. Such measurements, however, do not preserve information on the detailed, continuous pattern of articulatory timing that unfolds during these intervals. We present an approach that allows the analysis of entire, continuous kinematic trajectories obtained in a movement tracking experiment examining the influence of a phrasal boundary on articulatory patterning. FDA time deformation functions, after alignment of test and reference (control) signals, reveal delaying of articulator movement (i.e., slowing of the internal clock rate) in the presence of a phrase boundary as the speech stream recedes from the boundary. This is a theoretically predicted pattern (Byrd and Saltzman, 2003, The elastic phrase: Modeling the dynamics of boundary-adjacent lengthening, Journal of Phonetics 31, 149-180.), which would be more difficult to validate with a traditional interval-based approach. It is concluded that the FDA time alignment method provides a useful tool for characterizing timing patterns in linguistic experimentation based on continuous kinematic trajectories.

Humans↗

Gene selection for microarray data analysis using principal component analysis.

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure.

Gene Expression↗

EXPANDER--an integrative program suite for microarray data analysis.

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander.

Algorithms↗

SpectroPipeR-a streamlining post Spectronaut® DIA-MS data analysis R package.

SUMMARY: Proteome studies frequently encounter challenges in down-stream data analysis due to limited bioinformatics resources, rapid data generation, and variations in analytical methods. To address these issues, we developed SpectroPipeR, an R package designed to streamline data analysis tasks and provide a comprehensive, standardized pipeline for Spectronaut® DIA-MS data. This novel package automates various analytical processes, including XIC plots, ID rate summary, normalization, batch and covariate adjustment, relative protein quantification, multivariate analysis, and statistical analysis, while generating interactive HTML reports for e.g. ELN systems. AVAILABILITY AND IMPLEMENTATION: The SpectroPipeR package (manual: https://stemicha.github.io/SpectroPipeR/) was written in R and is freely available on GitHub (https://github.com/stemicha/SpectroPipeR).

Software↗

Exploratory data analysis of DNA microarrays by multivariate curve resolution.

In this work, the application of a multivariate curve resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.

Acute Disease↗