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At least 757 records · Page 42Linked to original sources
PubMed2007

Overestimating resistance in field testing of malaria parasites: simple methods for estimating high EC50 values using a Bayesian approach.

Conventional methods of assessing in-vitro antimalarial drug-concentration effect relationships in field testing of fresh isolates assess each parasite isolate individually. This leads to systematic overestimation of EC50 values for the most resistant isolates, and thus overestimation of the degree of resistance. In antimalarial drug-susceptibility studies conducted on the north-western border of Thailand the overestimation of EC50 for the most resistant isolate ranged from 15% for artesunate to 43% for mefloquine. If isolates cannot be stored for re-testing, more accurate estimations of the degree of resistance can be obtained using a Bayesian approach to data analysis which is described here.

Algorithms↗
PubMed2004

Coalescent-based association mapping and fine mapping of complex trait loci.

We outline a general coalescent framework for using genotype data in linkage disequilibrium-based mapping studies. Our approach unifies two main goals of gene mapping that have generally been treated separately in the past: detecting association (i.e., significance testing) and estimating the location of the causative variation. To tackle the problem, we separate the inference into two stages. First, we use Markov chain Monte Carlo to sample from the posterior distribution of coalescent genealogies of all the sampled chromosomes without regard to phenotype. Then, averaging across genealogies, we estimate the likelihood of the phenotype data under various models for mutation and penetrance at an unobserved disease locus. The essential signal that these models look for is that in the presence of disease susceptibility variants in a region, there is nonrandom clustering of the chromosomes on the tree according to phenotype. The extent of nonrandom clustering is captured by the likelihood and can be used to construct significance tests or Bayesian posterior distributions for location. A novelty of our framework is that it can naturally accommodate quantitative data. We describe applications of the method to simulated data and to data from a Mendelian locus (CFTR, responsible for cystic fibrosis) and from a proposed complex trait locus (calpain-10, implicated in type 2 diabetes).

Alleles↗
PubMed1994

Automated transformation of probabilistic knowledge for a medical diagnostic system.

Iliad is a large medical diagnostic system that covers more than 2000 diagnoses and 9000 findings. Due to the size and the complexity of this system, a robust knowledge representation is essential to consistently and efficiently model the medical knowledge involved. In this paper, we describe the knowledge representation currently used in Iliad and a probabilistic representation based on the Bayesian network formalism which can be derived using the information that the Iliad knowledge base contains.

Algorithms↗
PubMed2002

Bayesian methods for a three-state model for rodent carcinogenicity studies.

The objective of a chronic rodent bioassay is to assess the impact of a chemical compound on the development of tumors. However, most tumor types are not observable prior to necropsy, making direct estimation of the tumor incidence rate problematic. In such cases, estimation can proceed only if the study incorporates multiple interim sacrifices or we make use of simplified parametric or nonparametric models. In addition, it is widely accepted that other factors, such as weight, can be related to both dose level and tumor onset, confounding the association of interest. However, there is not typically enough information in the current study to assess such effects. The addition of historical data can help alleviate this problem. In this article, we propose a novel Bayesian semiparametric model for the analysis of data from rodent carcinogenicity studies. We develop informative prior distributions for covariate effects through the use of historical control data and outline a Gibbs sampling scheme. We implement the model by analyzing data from a National Toxicology Program chronic rodent bioassay.

Animals↗
PubMed2003

Bayesian latent variable models for median regression on multiple outcomes.

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.

Algorithms↗
PubMed2005

Risk analysis of a patient monitoring system using Bayesian Network modeling.

In a modern technological environment where information systems are characterized by complexity, situations of non-effective operation should be anticipated. Often system failures are a result of insufficient planning or equipment malfunction, indicating that it is essential to develop techniques for predicting and addressing a system failure. Particularly for safety-critical applications such as the healthcare information systems, which are dealing with human health, risk analysis should be considered a necessity. This paper presents a new method for performing a risk analysis study of health information systems. Specifically, the CCTA Risk Analysis and Management Methodology (CRAMM) has been utilized for identifying and valuating the assets, threats, and vulnerabilities of the information system, followed by a graphical modeling of their interrelationships using Bayesian Networks. The proposed method exploits the results of the CRAMM-based risk analysis for developing a Bayesian Network model, which presents concisely all the interactions of the undesirable events for the system. Based on "what-if" studies of system operation, the Bayesian Network model identifies and prioritizes the most critical events. The proposed risk analysis framework has been applied to a vital signs monitoring information system for homecare telemedicine, namely the VITAL-Home System, developed and maintained for a private medical center (Medical Diagnosis and Treatment S.A.).

Bayes Theorem↗
PubMed2006

Bayesian logistic regression using a perfect phylogeny.

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.

Bayes Theorem↗
PubMed2006

PSMIX: an R package for population structure inference via maximum likelihood method.

BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. RESULTS: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. CONCLUSION: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations.PSMIX and its supplemental documents are freely available at http://bioinformatics.med.yale.edu/PSMIX.

Algorithms↗
PubMed1985

A nonlinear multiple regression program, MULTI2 (BAYES), based on Bayesian algorithm for microcomputers.

A nonlinear multiple regression analysis program MULTI2(BAYES) was developed for microcomputers. The Bayesian algorithm which is incorporated in MULTI2 (BAYES) combines the insufficient individual patient data (individual data) with the pharmacokinetic parameters published in literatures (population parameters) to predict the plasma time course of the patient. The program is written in the minimum Microsoft BASIC commands alone to be executable on many personal computers without any modification. The numbers of parameters to estimate, independent variables and dependent variables are not restricted in use of MULTI2(BAYES). The pharmacokinetic models are defined as one pleases by the user. The four nonlinear least squares algorithms, i.e. Gauss-Newton method, damping Gauss-Newton method, modified Marquardt method by Fletcher and simplex method can be selected at user's option. MULTI2(BAYES) calculates the confidence limits of time courses at 95% significant level.

Computers↗
PubMed2006

A segmentation-based regularization term for image deconvolution.

This paper proposes a new and original inhomogeneous restoration (deconvolution) model under the Bayesian framework for observed images degraded by space-invariant blur and additive Gaussian noise. In this model, regularization is achieved during the iterative restoration process with a segmentation-based a priori term. This adaptive edge-preserving regularization term applies a local smoothness constraint to pre-estimated constant-valued regions of the target image. These constant-valued regions (the segmentation map) of the target image are obtained from a preliminary Wiener deconvolution estimate. In order to estimate reliable segmentation maps, we have also adopted a Bayesian Markovian framework in which the regularized segmentations are estimated in the maximum a posteriori (MAP) sense with the joint use of local Potts prior and appropriate Gaussian conditional luminance distributions. In order to make these segmentations unsupervised, these likelihood distributions are estimated in the maximum likelihood sense. To compute the MAP estimate associated to the restoration, we use a simple steepest descent procedure resulting in an efficient iterative process converging to a globally optimal restoration. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art methods in benchmark tests.

Algorithms↗
PubMed2005

Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data.

MOTIVATION: Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously. A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene. Several approaches to model gene-specific variances are proposed. We take advantage of calibration experiments, in which the probes hybridized on the two channels come from the same population (self-self experiment). In this case it is possible to estimate the gene-specific variance, to be incorporated in comparative experiments on the same tissue, cellular line or species. RESULTS: We present two approaches to introduce prior information on gene-specific variability from a calibration experiment: an empirical Bayes model and a full Bayesian hierarchical model. We apply the methods in the analysis of human lipopolysaccharide-stimulated leukocyte experiments. AVAILABILITY: The calculations are implemented in WinBugs. The codes are available on request from the authors.

Algorithms↗
PubMed2005

Bayesian networks: computer-assisted diagnosis support in radiology.

Medical knowledge is growing at an explosive rate. While the availability of pertinent data has the potential to make the task of diagnosis more accurate, it is also increasingly overwhelming for physicians to assimilate. Using artificial intelligence techniques, a computer can process large amounts of data to help physicians manage the growing body of medical knowledge and thereby make better decisions. Computer-assisted diagnosis support is of particular interest to the diagnostic imaging community because radiologists must integrate huge amounts of data in order to diagnose disease. Bayesian networks, among the most promising artificial intelligence techniques available, enable computers to store knowledge and estimate the probability of outcomes based on probability theory. The article describes what a Bayesian network is and how it works using a system in mammography for illustration. A comparison of Bayesian networks with other types of artificial intelligence methods, specifically neural networks and case-based reasoning, clarifies the unique features and the potential of these systems to aid radiologists in the decisions they make every day.

Bayes Theorem↗
PubMed2005

A Bayesian mixture model relating dose to critical organs and functional complication in 3D conformal radiation therapy.

A goal of cancer radiation therapy is to deliver maximum dose to the target tumor while minimizing complications due to irradiation of critical organs. Technological advances in 3D conformal radiation therapy has allowed great strides in realizing this goal; however, complications may still arise. Critical organs may be adjacent to tumors or in the path of the radiation beam. Several mathematical models have been proposed that describe the relationship between dose and observed functional complication; however, only a few published studies have successfully fit these models to data using modern statistical methods which make efficient use of the data. One complication following radiation therapy of head and neck cancers is the patient's inability to produce saliva. Xerostomia (dry mouth) leads to high susceptibility to oral infection and dental caries and is, in general, unpleasant and an annoyance. We present a dose-damage-injury model that subsumes any of the various mathematical models relating dose to damage. The model is a nonlinear, longitudinal mixed effects model where the outcome (saliva flow rate) is modeled as a mixture of a Dirac measure at zero and a gamma distribution whose mean is a function of time and dose. Bayesian methods are used to estimate the relationship between dose delivered to the parotid glands and the observational outcome-saliva flow rate. A summary measure of the dose-damage relationship is modeled and assessed by a Bayesian chi(2) test for goodness-of-fit.

Bayes Theorem↗
PubMed1998

Safety and efficacy of a two-compartment Bayesian feedback program for therapeutic tobramycin monitoring in the daily clinical use and comparison with a non-Bayesian one-compartment model.

The predictive value of a two-compartment Bayesian feedback program for tobramycin dose optimization was retrospectively evaluated in 199 hospitalized patients and compared with that of a simple non-Bayesian one-compartment model. Before dose adjustment, 64% of the patients were underdosed indicating that tobramycin monitoring is still necessary to avoid ineffective antibiotic therapy. When physicians adhered to the dose instructions calculated with the Bayesian method, 90% of the patients had optimal concentration-time profiles as opposed to only 53% of the 43 patients in whom dose recommendations were not followed. In young patients with normal renal function, precision and accuracy of the Bayesian feedback and the one-compartment method were well correlated, whereas in elderly patients (> 60 years) and patients with impaired renal function (estimated creatinine clearance < 60 ml/minute), the Bayesian method was significantly more precise. Multiple regression analysis revealed that renal function was the only independent variable predicting the performance of the Bayesian program. The results of this study indicate that the Bayesian feedback method is a reliable method for the therapeutic tobramycin monitoring under clinical conditions and in particular, elderly patients in whom renal impairment is frequent.

Absorption↗
PubMed1994

A Bayesian approach to subvoxel tissue classification in NMR microscopic images of trabecular bone.

NMR microscopy is currently being used as an investigational tool for the evaluation of micromorphometric parameters of trabecular bone as a possible means to assess its strength. Since, typically, the image voxel size is not significantly smaller than individual trabecular elements, partial volume blurring can be a major complication for accurate tissue classification. In this paper, a Bayesian segmentation technique is reported that achieves improved subvoxel tissue classification. Each voxel is subdivided either into eight subvoxels twice the original resolution, or up to four subvoxels along the transaxial direction and the subvoxels optimally classified as volume blurring, the likelihood for the number of marrow subvoxels in each voxel can be computed on the basis of its measured signal. To resolve the ambiguity of the location of the marrow subvoxels, a Gibbs distribution is introduced to model the interaction between the subvoxels. Neighboring subvoxel pairs with the same tissue label are encouraged, and pairs with distinct labels are penalized. The segmentation is achieved by maximizing the a posteriori probability of the label image using the block ICM (iterative conditional mode) algorithm. The potential of the proposed technique is demonstrated in real and synthetic NMR microscopic images.

Algorithms↗
PubMed2003

A new phylogenetic marker, apolipoprotein B, provides compelling evidence for eutherian relationships.

Higher-level relationships within, and the root of Placentalia, remain contentious issues. Resolution of the placental tree is important to the choice of mammalian genome projects and model organisms, as well as for understanding the biogeography of the eutherian radiation. We present phylogenetic analyses of 63 species representing all extant eutherian mammal orders for a new molecular phylogenetic marker, a 1.3kb portion of exon 26 of the apolipoprotein B (APOB) gene. In addition, we analyzed a multigene concatenation that included APOB sequences and a previously published data set (Murphy et al., 2001b) of three mitochondrial and 19 nuclear genes, resulting in an alignment of over 17kb for 42 placentals and two marsupials. Due to computational difficulties, previous maximum likelihood analyses of large, multigene concatenations for placental mammals have used quartet puzzling, less complex models of sequence evolution, or phylogenetic constraints to approximate a full maximum likelihood bootstrap. Here, we utilize a Unix load sharing facility to perform maximum likelihood bootstrap analyses for both the APOB and concatenated data sets with a GTR+Gamma+I model of sequence evolution, tree-bisection and reconnection branch-swapping, and no phylogenetic constraints. Maximum likelihood and Bayesian analyses of both data sets provide support for the superordinal clades Boreoeutheria, Euarchontoglires, Laurasiatheria, Xenarthra, Afrotheria, and Ostentoria (pangolins+carnivores), as well as for the monophyly of the orders Eulipotyphla, Primates, and Rodentia, all of which have recently been questioned. Both data sets recovered an association of Hippopotamidae and Cetacea within Cetartiodactyla, as well as hedgehog and shrew within Eulipotyphla. APOB showed strong support for an association of tarsier and Anthropoidea within Primates. Parsimony, maximum likelihood and Bayesian analyses with both data sets placed Afrotheria at the base of the placental radiation. Statistical tests that employed APOB to examine a priori hypotheses for the root of the placental tree rejected rooting on myomorphs and hedgehog, but did not discriminate between rooting at the base of Afrotheria, at the base of Xenarthra, or between Atlantogenata (Xenarthra+Afrotheria) and Boreoeutheria. An orthologous deletion of 363bp in the aligned APOB sequences proved phylogenetically informative for the grouping of the order Carnivora with the order Pholidota into the superordinal clade Ostentoria. A smaller deletion of 237-246bp was diagnostic of the superordinal clade Afrotheria.

Amino Acid Sequence↗
PubMed2006

Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.

Algorithms↗
PubMed2001

A case study on the choice, interpretation and checking of multilevel models for longitudinal binary outcomes.

Recent advances in statistical software have led to the rapid diffusion of new methods for modelling longitudinal data. Multilevel (also known as hierarchical or random effects) models for binary outcomes have generally been based on a logistic-normal specification, by analogy with earlier work for normally distributed data. The appropriate application and interpretation of these models remains somewhat unclear, especially when compared with the computationally more straightforward semiparametric or 'marginal' modelling (GEE) approaches. In this paper we pose two interrelated questions. First, what limits should be placed on the interpretation of the coefficients and inferences derived from random-effect models involving binary outcomes? Second, what diagnostic checks are appropriate for evaluating whether such random-effect models provide adequate fits to the data? We address these questions by means of an extended case study using data on adolescent smoking from a large cohort study. Bayesian estimation methods are used to fit a discrete-mixture alternative to the standard logistic-normal model, and posterior predictive checking is used to assess model fit. Surprising parallels in the parameter estimates from the logistic-normal and mixture models are described and used to question the interpretability of the so-called 'subject-specific' regression coefficients from the standard multilevel approach. Posterior predictive checks suggest a serious lack of fit of both multilevel models. The results do not provide final answers to the two questions posed, but we expect that lessons learned from the case study will provide general guidance for further investigation of these important issues.

Journal Article↗