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PubMed2001

Support of diagnosis of liver disorders based on a causal Bayesian network model.

We describe our work on HEPAR II, a probabilistic causal model for diagnosis of liver disorders. The model, a Bayesian network capturing the causal interactions among various risk factors, diseases, symptoms, and test results, is based on expert knowledge combined with clinical data captured in medical records. The main applications of HEPAR II are assistance is diagnosis and training of beginning diagnosticians. We outline the principles of the applied approach, present a brief description of the model, and report its diagnostic performance.

Algorithms↗
PubMed2005

A hybrid Bayesian adaptive design for dose response trials.

In recent years, the use of adaptive design methods based on accrued data of on-going trials have become very popular for dose response trials in early clinical development due to their flexibility (EMEA, 2002). In this paper, we developed a hybrid frequentist-Bayesian continual reassessment method (CRM) in conjunction with utility-adaptive randomization for clinical trial designs with multiple endpoints. The proposed hyperlogistic function family with multiple parameters gives users flexibility for probability modeling. CRM reassesses a dose-response relationship based on accrued data of the on-going trial, which allows investigators to make decisions based on a constantly updated dose-response model. The proposed utility-adaptive randomization for multiple-endpoint trials allows more patients to be assigned to superior treatment groups. The performance of the proposed method was examined in terms of its operating characteristics through computer simulations.

Algorithms↗
PubMed2007

Estimation of population pharmacokinetic parameters of saquinavir in HIV patients with the MONOLIX software.

In nonlinear mixed-effects models, estimation methods based on a linearization of the likelihood are widely used although they have several methodological drawbacks. Kuhn and Lavielle (Comput. Statist. Data Anal. 49:1020-1038 (2005)) developed an estimation method which combines the SAEM (Stochastic Approximation EM) algorithm, with a MCMC (Markov Chain Monte Carlo) procedure for maximum likelihood estimation in nonlinear mixed-effects models without linearization. This method is implemented in the Matlab software MONOLIX which is available at http://www.math.u-psud.fr/~lavielle/monolix/logiciels. In this paper we apply MONOLIX to the analysis of the pharmacokinetics of saquinavir, a protease inhibitor, from concentrations measured after single dose administration in 100 HIV patients, some with advance disease. We also illustrate how to use MONOLIX to build the covariate model using the Bayesian Information Criterion. Saquinavir oral clearance (CL/F) was estimated to be 1.26 L/h and to increase with body mass index, the inter-patient variability for CL/F being 120%. Several methodological developments are ongoing to extend SAEM which is a very promising estimation method for population pharmacockinetic/pharmacodynamic analyses.

Administration, Oral↗
PubMed2005

Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas.

The present article deals with individual probabilities of different levels of injury in case of a motorcycle accident. The approach uses an empirical Bayesian method based on the Multinomial-Dirichlet model, see [Leonard, T., 1977. A Bayesian approach to some Multinomial estimation and pretesting problems, J. Am. Stat. Association, 72, 869-874], to conduct an analysis of the probability distributions about the severity of accidents at the level of individuals in large and dense French urban areas during year 2003. We model accident severity using four levels of injury: material damages only, slight injury, severe injury, fatal injury. Our application shows that sociodemographic characteristics of motorcyclists and factors influencing their speed behaviors, the suddenness of their collision and the vigilance of road users play significant roles on the shapes of their probability distributions of accident severity. The computation of posterior distributions of the levels of injury for different groups of motorcyclists enables us to rank them with respect to their risk of injury using second order stochastic dominance orderings. It is found that women motorcyclists between 30 and 50 years old driving powerful motorcycles are the most exposed to risk of injury.

Accidents, Traffic↗
PubMed1999

RNA secondary structure prediction using stochastic context-free grammars and evolutionary history.

MOTIVATION: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures. RESULTS: The phylogenetic tree relating the sequences can be found by maximum likelihood (ML) estimation from the model introduced here. The tree is shown to reveal information about the structure, due to mutation patterns. The inclusion of a prior distribution of RNA structures ensures good structure predictions even for a small number of related sequences. Prediction is carried out using maximum a posteriori estimation (MAP) estimation in a Bayesian approach. For small sequence sets, the method performs very well compared to current automated methods.

Algorithms↗
PubMed2001

Inferring subnetworks from perturbed expression profiles.

Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.

Bayes Theorem↗
PubMedDate not supplied

Revision of diagnostic logic using a clinical database.

Statistical pattern-recognition techniques have been frequently applied to the problem of medical diagnosis. Sequential Bayesian approaches are appealing because of the possibility of generating the underlying sensitivities, specificities, and prevalence statistics from the estimates of medical experts. The accuracy of these estimates and the consequences of inaccuracies carry implications for the future development of this type of system. In an effort to explore these subjects, the authors used statistics derived from a clinical database to revise the diagnostic logic in a Bayesian system for generating a differential diagnostic list. Substantial changes in estimated a priori probabilities, sensitivities, and specificities were made to correct for significant under- and overestimations of these values by a group of medical experts. The system based on the derived values appears to perform better than the original system. It is concluded that the statistics used in a Bayesian diagnostic system should be derived from a database representative of the patient population for which the system is designed.

Bayes Theorem↗
PubMed2006

On locating multiple interacting quantitative trait loci in intercross designs.

A modified version (mBIC) of the Bayesian Information Criterion (BIC) has been previously proposed for backcross designs to locate multiple interacting quantitative trait loci. In this article, we extend the method to intercross designs. We also propose two modifications of the mBIC. First we investigate a two-stage procedure in the spirit of empirical Bayes methods involving an adaptive (i.e., data-based) choice of the penalty. The purpose of the second modification is to increase the power of detecting epistasis effects at loci where main effects have already been detected. We investigate the proposed methods by computer simulations under a wide range of realistic genetic models, with nonequidistant marker spacings and missing data. In the case of large intermarker distances we use imputations according to Haley and Knott regression to reduce the distance between searched positions to not more than 10 cM. Haley and Knott regression is also used to handle missing data. The simulation study as well as real data analyses demonstrates good properties of the proposed method of QTL detection.

Algorithms↗
PubMed2004

Modeling cellular processes with variational Bayesian cooperative vector quantizer.

Gene expression of a cell is controlled by sophisticated cellular processes. The capability of inferring the states of these cellular processes would provide insight into the mechanism of gene expression control system. In this paper, we propose and investigate the cooperative vector quantizer (CVQ) model for analysis of microarray data. The CVQ model could be capable of decomposing observed microarray data into many different regulatory subprocesses. To make the CVQ analysis tractable we develop and apply variational approximations. Bayesian model selection is employed in the model, so that the optimal number processes is determined purely from observed micro-array data. We test the model and algorithms on two datasets: (1) simulated gene-expression data and (2) real-world yeast cell-cycle microarray data. The results illustrate the ability of the CVQ approach to recover and characterize regulatory gene expression subprocesses, indicating a potential for advanced gene expression data analysis.

Algorithms↗
PubMed2004

Modeling T-cell activation using gene expression profiling and state-space models.

MOTIVATION: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. RESULTS: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. AVAILABILITY: Supplementary data and Matlab computer source code will be made available on the web at the URL given below. SUPPLEMENTARY INFORMATION: http://public.kgi.edu/~wild/LDS/index.htm

Computer Simulation↗
PubMed2004

The Bayesian brain: the role of uncertainty in neural coding and computation.

To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action. Bayesian methods have proven successful in building computational theories for perception and sensorimotor control, and psychophysics is providing a growing body of evidence that human perceptual computations are "Bayes' optimal". This leads to the "Bayesian coding hypothesis": that the brain represents sensory information probabilistically, in the form of probability distributions. Several computational schemes have recently been proposed for how this might be achieved in populations of neurons. Neurophysiological data on the hypothesis, however, is almost non-existent. A major challenge for neuroscientists is to test these ideas experimentally, and so determine whether and how neurons code information about sensory uncertainty.

Animals↗
PubMed2005

Protein structure elucidation from minimal NMR data: the CLOUDS approach.

In this chapter we review automated methods of protein NMR data analysis and expand on the assignment-independent CLOUDS approach. As presented, given a set of reliable NOEs it is feasible to derive a spatial H-atom distribution that provides a low-resolution image of the protein structure. In order to generate such a list of unambiguous NOEs, a probabilistic assessment of the NOE identities (in terms of frequency-labeled H-atom sources) was developed on the basis of Bayesian inference. The methodology, encompassing programs SPI and BACUS, provides a list of "clean" NOEs that does not hinge on prior knowledge of sequence-specific resonance assignments or a preliminary structural model. As such, the combined SPI/BACUS approach, intrinsically adaptable to include 13C- and/or 15N-edited experiments, affords a useful tool for the analysis of NMR data irrespective of whether the adopted structure calculation protocol is assignment-dependent.

Bayes Theorem↗
PubMed2006

Evolutionary changes of metabolic networks and their biosynthetic capacities.

The metabolic networks of different species show a large variety in their structural design. In this work, the evolution of functional properties of metabolism in relation with metabolic network structure is investigated. The metabolism of ancestral species is inferred from the metabolism of contemporary species using a Bayesian network model for metabolism evolution. Subsequently, these networks are analysed with the recently developed method of network expansion. This method allows for a structural analysis of metabolic networks as well as a quantification of network functions in terms of their synthesising capacities when they are provided with certain external resources. The evolutionary dynamics of one particular network function: the metabolic expansion of glucose is investigated.

Adaptation, Physiological↗
PubMed1993

Pooled-sampling makes high-resolution mapping practical with DNA markers.

A pooled-sample approach to the construction of high-resolution genetic maps is described. The strategy depends on the existence of an easily selectable target locus and the ability to produce large segregating populations. If these requirements are met, the pooled-sample mapping approach allows tightly linked markers (e.g., restriction fragment length polymorphisms) to be mapped relative to the target with a great economy of effort. The recombination fractions among loci can be estimated by the maximum likelihood method and a simple approximate estimator is derived. The order of loci is deduced using a Bayesian statistical framework to yield posterior probabilities for all possible orderings of a marker set. Optimal pooling strategies and the effects of misclassification of selected individuals are discussed and studied by computer simulation. The feasibility of this method is demonstrated by the high-resolution mapping of a region on chromosome 5 of tomato that contains a gene regulating fruit ripening.

Animals↗
PubMed2003

Interactive decision support system to predict print quality.

Customers using printers occasionally experience problems such as fuzzy images, bands, or streaks. The customer may call or otherwise contact the manufacturer, who attempts to diagnose the problem based on the customer's description of the problem. This study evaluated Bayesian inference as a tool for identifying or diagnosing 16 different types of print defects from such descriptions. The Bayesian model was trained using 1701 narrative descriptions of print defects obtained from 60 subjects with varying technical backgrounds. The Bayesian model was then implemented as an interactive decision support system, which was used by eight 'agents' to diagnose print defects reported by 16 'customers' in a simulated call centre. The 'agents' and 'customers' in the simulated call centre were all students at Purdue University. Each customer made eight telephone calls, resulting in a total of 128 telephone calls in which the customer reported defects to the agents. The results showed that the Bayesian model closely fitted the data in the training set of narratives. Overall, the model correctly predicted the actual defect category with its top prediction 70% of the time. The actual defect was in the top five predictions 94% of the time. The model in the simulated call centre performed nearly as well for the test subjects. The top prediction was correct 50% of the time, and the defect was one of the top five predictions 80% of the time. Agent accuracy in diagnosing the problem improved when using the tool. These results demonstrated that the Bayesian system learned enough from the existing narratives to accurately classify print defect categories.

Adolescent↗
PubMed2002

Bayesian nonstationary autoregressive models for biomedical signal analysis.

We describe a variational Bayesian algorithm for the estimation of a multivariate autoregressive model with time-varying coefficients that adapt according to a linear dynamical system. The algorithm allows for time and frequency domain characterization of nonstationary multivariate signals and is especially suited to the analysis of event-related data. Results are presented on synthetic data and real electroencephalogram data recorded in event-related desynchronization and photic synchronization scenarios.

Algorithms↗
PubMed2005

Multiple transatlantic introductions of the western corn rootworm.

The invasion of Europe by the western corn rootworm, North America's most destructive corn pest, is ongoing and represents a serious threat to European agriculture. Because this pest was initially introduced in Central Europe, it was believed that subsequent outbreaks in Western Europe originated from this area. Using model-based Bayesian analyses of the genetic variability of the western corn rootworm, we demonstrate that this belief is false: There have been at least three independent introductions from North America during the past two decades. This result raises questions about changing circumstances that have enabled a sudden burst of transatlantic introductions.

Animals↗
PubMed2005

Monitoring event times in early phase clinical trials: some practical issues.

BACKGROUND: In many early phase clinical trials it is scientifically inappropriate or logistically infeasible to characterize patient outcome as a binary variable. In such settings, it often is more natural to construct early stopping rules based on time-to-event variables. This type of design may involve a variety of complications, however. PURPOSE: The purpose of this paper is to illustrate by example how one may deal with various complications that may arise when monitoring time-to-event outcomes in an early phase clinical trial. METHODS: We present a series of Bayesian designs for a phase II clinical trial in kidney cancer. Each design includes a procedure for monitoring the times to a severe adverse event, disease progression and death. The first design, which is the simplest, is based on the time to failure, defined as any of the three events, assuming exponentially distributed failure times with an inverse gamma prior on the mean. This design is compared by simulation to the CMAP design (Cheung and Thall, Biometrics, 2002; 58: 89-97). The model and monitoring procedure are then extended successively to accommodate several common practical complications, and we also study the method's robustness. RESULTS: Our simulations show that 1) one may apply the monitoring rule periodically, rather than continuously, without a substantive degradation of the design's reliability; 2) it is very important to account for interval censoring due to periodic evaluation of disease status; 3) it is important to account for the effect of disease progression on the subsequent death rate; 4) conducting a randomized trial presents little additional difficulty and provides unbiased comparisons; and 5) the exponential-inverse gamma model is surprisingly robust in most cases. LIMITATIONS: The methods discussed here do not account for patient heterogeneity. This is an important but complex issue that may be dealt with by extending the models and methods given here to accommodate patient covariates and treatment-covariate interaction. CONCLUSIONS: Bayesian procedures for monitoring time-to-event outcomes offer a practical way to conduct a variety of early phase trials. Considerable care must be given, however, to modeling the important aspects of the trial at hand, and to calibrating the prior and the design parameters to ensure that the design will have good operating characteristics.

Bayes Theorem↗