Search results for "Data Interpretation"
showing 10 items of 195 documents
Predicting antitrichomonal activity: A computational screening using atom-based bilinear indices and experimental proofs
2006
Existing Trichomonas vaginalis therapies are out of reach for most trichomoniasis people in developing countries and, where available, they are limited by their toxicity (mainly in pregnant women) and their cost. New antitrichomonal agents are needed to combat emerging metronidazole-resistant trichomoniasis and reduce the side effects associated with currently available drugs. Toward this end, atom-based bilinear indices, a new TOMOCOMD-CARDD molecular descriptor, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic atom-type bilinear in…
Polimorfismos en el gen de la apolipoproteína E y riesgo de hipercolesterolemia: un estudio de casos y controles en una población laboral de Valencia
2000
Fundamento El gen de la apolipoproteina E (apoE) presenta tres variantes comunes (alelos ɛ2, ɛ3, ɛ4), cuya frecuencia alelica y asociacion con los lipidos plasmaticos varia segun las poblaciones. Nuestro objetivo fue estimar la asociacion entre estas variantes geneticas y el riesgo de hipercolesterolemia en la poblacion mediterranea espanola. Pacientes y metodo Se ha realizado un estudio de casos y controles en una poblacion laboral de Valencia. Se identificaron 330 casos (148 varones y 182 mujeres) con hipercolesterolemia moderada (colesterol total > 200 mg/dl o con tratamiento hipolipemiante) y un intervalo de edad entre 20–60 anos. Se seleccionaron 330 controles normocolesterolemicos apa…
Running to keep up with the lecturer or gradual de-ritualization? Biology students’ engagement with construction and data interpretation graphing rou…
2021
Abstract Through a commognitive lens, we examine twelve first-semester biology students’. engagement with graphing routines as they work in groups, during four sessions of Mathematical Modelling (MM). We trace the students’ meta-level learning, particularly as they fluctuate between deploying graphs for mere illustration of data and as sense-making tools. We account for student activity in relation to precedent events in their experiences of graphing and as fluid, if not always productive, interplay between ritualised and exploratory engagement with graph construction and interpretation routines. The students’ construal of the task situations is marked by efforts to keep up with lecturer ex…
A neural network approach to movement pattern analysis.
2004
Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, …
Statistical analysis of latency outcomes in behavioral experiments
2011
In experimental designs of animal models, memory is often assessed by the time for a performance measure to occur (latency). Depending on the cognitive test, this may be the time it takes an animal to escape to a hidden platform (water maze), an escape tunnel (Barnes maze) or to enter a dark component (passive avoidance test). Latency outcomes are usually statistically analyzed using ANOVAs. Besides strong distributional assumptions, ANOVA cannot properly deal with animals not showing the performance measure within the trial time, potentially causing biased and misleading results. We propose an alternative approach for statistical analyses of latency outcomes. These analyses have less distr…
Detection of spatial disease clusters with LISA functions.
2011
Detection of disease clusters is an important tool in epidemiology that can help to identify risk factors associated with the disease and in understanding its etiology. In this article we propose a method for the detection of spatial clusters where the locations of a set of cases and a set of controls are available. The method is based on local indicators of spatial association functions (LISA functions), particularly on the development of a local version of the product density, which is a second-order characteristic of spatial point processes. The behavior of the method is evaluated and compared with Kulldorff's spatial scan statistic by means of a simulation study. It is shown that the LI…
Testing for homogeneity in meta-analysis I. The one-parameter case: standardized mean difference.
2010
Meta-analysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result. Cochran's Q statistic is frequently used for this homogeneity test. It is often assumed that Q follows a chi-square distribution under the null hypothesis of homogeneity, but it has long been known that this asymptotic distribution for Q is not accurate for moderate sample sizes. Here, we present an expansion for the mean of Q under the null hypothesis that is valid when the effect and the weight for each s…
Cluster-Localized Sparse Logistic Regression for SNP Data
2012
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…
Multiple testing in candidate gene situations: a comparison of classical, discrete, and resampling-based procedures.
2011
In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SN…
A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance
2018
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type …