Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Bayesian computational modeling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17Linked to original sources
PubMedDate not supplied

Practical model-based dose-finding in phase I clinical trials: methods based on toxicity.

We describe two practical, outcome-adaptive statistical methods for dose-finding in phase I clinical trials. One is the continual reassessment method and the other is based on a logistic regression model. Both methods use Bayesian probability models as a basis for learning from the accruing data during the trial, choosing doses for successive patient cohorts, and selecting a maximum tolerable dose (MTD). These methods are illustrated and compared to the conventional 3+3 algorithm by application to a particular trial in renal cell carcinoma. We also compare their average behavior by computer simulation under each of several hypothetical dose-toxicity curves. The comparisons show that the Bayesian methods are much more reliable than the conventional algorithm for selecting an MTD, and that they have a low risk of treating patients at unacceptably toxic doses.

Algorithms↗
PubMedDate not supplied

The psychology of good judgment: frequency formats and simple algorithms.

Mind and environment evolve in tandem--almost a platitude. Much of judgment and decision making research, however, has compared cognition to standard statistical models, rather than to how well it is adapted to its environment. The author argues two points. First, cognitive algorithms are tuned to certain information formats, most likely to those that humans have encountered during their evolutionary history. In particular, Bayesian computations are simpler when the information is in a frequency format than when it is in a probability format. The author investigates whether frequency formats can make physicians reason more often the Bayesian way. Second cognitive algorithms need to operate under constraints of limited time, knowledge, and computational power, and they need to exploit the structures of their environments. The author describes a fast and frugal algorithm. Take The Best, that violates standard principles of rational inference but can be as accurate as sophisticated "optimal" models for diagnostic inference.

Algorithms↗
PubMed2005

Estimation of fugitive lead emission rates from secondary lead facilities using hierarchical Bayesian models.

Fugitive emissions from secondary lead recovery facilities are difficult to estimate and can vary significantly from site to site. A methodology is presented for estimating fugitive emissions using back inference from observed ambient concentrations at nearby monitors, in conjunction with an atmospheric transport and dispersion model. Observed concentrations are regressed against unit source-monitor transfer terms computed by the model, and the fitted parameters of the regression equation include the background ambient lead concentration, the fugitive lead emission rate, and (when stack emissions are assumed to be unknown) the stack lead emission rate. The methodology is implemented at three sites, one each in Florida, Texas, and New York. A hierarchical Bayesian method is used to estimate the parameters of the model, allowing inferences to be made for both site-specific values and multisite (national) distributions of fugitive emissions and background concentrations. Informed prior distributions must be specified for the background lead concentrations and for fugitive and stack emission rates in order to obtain stable estimates. Sensitivity analyses with alternative priors indicate that posterior estimates of background concentrations and fugitive emission rates are relatively insensitive to the assumed priors, although estimated stack emission rates can vary with alternative priors, especially for the New York facility, where the stack emission rate is highly uncertain and poorly resolved by the model. The fugitive lead emission rates estimated for the sites are comparable to, or in some cases (especially Texas and New York) likely larger than the stack emissions that are determined for these facilities. An aggregate predictive distribution is derived for the average fugitive lead emission rate from secondary lead smelting facilities, with a median value of 9.2 x 10(-7) g Pb/m2/sec, and a 90% credible interval from 2.1 x 10(-7)-5.3 x 10(-6) g Pb/m2/sec. This wide range reflects both the variation in fugitive lead emissions from site to site and the high degree of uncertainty resulting from an estimate based on only a very small sample of sites. As such, the primary contribution of this study is methodological, demonstrating how information from multiple sites can be combined and considered simultaneously for the estimation of fugitive emission rates, but recognizing that additional sites must be included to obtain a more precise characterization.

Air Pollutants↗
PubMed2004

A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience.

OBJECTIVE: We sought to determine whether a probabilistic expert system can provide accurate automated imaging-histologic correlations to aid radiologists in assessing the concordance of mammographic findings with the results of imaging-guided breast biopsies. MATERIALS AND METHODS: We created a Bayesian network in which Breast Imaging Reporting and Data System (BI-RADS) descriptors are used to convey the level of suspicion of mammographic abnormalities. Our system is a computer model that links BI-RADS descriptors with diseases of the breast using probabilities derived from the literature. Mammographic findings are used to update pretest probabilities (prevalence of disease) into posttest probabilities applying Bayes' theorem. We evaluated the histologic results of 92 consecutive imaging-guided breast biopsies for concordance with the mammographic findings during radiology-pathology review sessions. First, radiologists with no knowledge of the biopsy results chose BI-RADS descriptors for the mammographic findings. After the histologic diagnosis was revealed, the radiologists assessed concordance between the pathologic results and the mammographic findings. We then input the information gathered from these sessions into the Bayesian network to produce an automated mammographic-histologic correlation. RESULTS: We had a sampling error rate of 1.1% (1/92 biopsies). Our expert system was able to integrate pathologic diagnoses and mammographic findings to obtain probabilities of sampling error, thereby enabling us to identify the incorrect pathologic diagnosis with 100% sensitivity while maintaining a specificity of 91%. CONCLUSION: Our probabilistic expert system has the potential to help radiologists in identifying breast biopsy results that are discordant with mammographic findings and discovering cases in which biopsy sampling errors may have occurred.

Adult↗
PubMed1997

A Bayesian formulation of the Kalman filter applied to the estimation of individual pharmacokinetic parameters.

A general method of Bayesian forecasting employing the dynamic linear model has been adapted to the problem of estimating individual pharmacokinetic parameters. The Bayesian forecasting method incorporates an efficient Kalman filter algorithm for updating pharmacokinetic parameter estimates when further observations are made. The Kalman filter is more general and flexible than other Bayesian methods currently used and simulation studies have demonstrated its practicality for three different pharmacokinetic models. The method serves as the basis for a computer program for general clinical use.

Algorithms↗
PubMed2002

Multimodality in the superior colliculus: an information theoretic analysis.

The deep superior colliculus (DSC) integrates multisensory input and triggers an orienting movement toward the source of stimulation (target). It would seem reasonable to suppose that input of an additional modality should always increase the amount of information received by a DSC neuron concerning a target. However, of all DSC neurons studied, only about one half in the cat and one-quarter in the monkey were multimodal. The rest received only unimodal input. Multimodal DSC neurons show the properties of multisensory enhancement, in which the neural response to an input of one modality is augmented by input of another modality, and of inverse effectiveness, in which weaker unimodal responses produce a higher percentage enhancement. Previously, we demonstrated that these properties are consistent with the hypothesis that DSC neurons use Bayes' rule to compute the posterior probability that a target is present given their stochastic sensory inputs. Here we use an information theoretic analysis of our Bayesian model to show that input of an additional modality may indeed increase target information, but only if input received from the initial modality does not completely reduce uncertainty concerning the presence of a target. Unimodal DSC neurons may be those whose unimodal input fully reduces target uncertainty and therefore have no need for input of another modality.

Bayes Theorem↗
PubMed2007

Technical note: how to use Winbugs to draw inferences in animal models.

This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree. Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature.

Animals↗
PubMedDate not supplied

Population pharmacokinetics of cisplatin in patients with advanced ovarian cancer during intraperitoneal hyperthermia chemotherapy.

The objective of this study was to determine the pharmacokinetic profile of total platinum administered by hyperthermic peritoneal perfusion (HPP) in 16 patients with ovarian cancer. The patients had a performance status of lIIalpha/b/c on the FIGO scale. They received 60, 80 or 100 mg of cisplatin. The percent of cisplatin remaining in the body after the peritoneum was emptied averaged 65% (41.7-85.4%). The average ratio between peritoneal drug concentrations and plasma concentrations was 73. A Bayesian estimation of individual phamacokinetic parameters was carried out using the non-linear mixed-effect modeling approach as implemented in the NONMEM computer program. A two-compartment model with an additional peritoneal cavity compartment was used to fit the data. Large interindividual variability of the pharmacokinetic parameters occurred The maximum platinum concentration in plasma was reached between 1 and 1.5 hours after the beginning of administration; it ranged from 0.37 to 1.7 microg/ml (1.9 to 8.72 microM). The elimination half-life was 80 hours (48-152 hours and the area under the plasma concentration time curve normalized to a 100 mg cisplatin dose was 79 mg/liter x hours. The simultaneous fit of perfusate and plasma concentrations allowed us to estimate the percent of cisplatin that reached the systemic circulation at about 20%. At time infinity, the urinary cisplatin recovery accounted for only 20% of the administered dose. The results in this study showed that a high proportion of the cisplatin dose was absorbed by target tumor cells. In spite of the advanced disease of patients at the time of HPP, 37.5% of them were still alive three years after HPP (ie., 3-6 years after cancer diagnosis) and 12.5%, 7 years after HPP (i.e., 8 years after cancer diagnosis).

Adult↗
PubMed1991

The USC*PACK PC programs for population pharmacokinetic modeling, modeling of large kinetic/dynamic systems, and adaptive control of drug dosage regimens.

Programs for PC's and compatibles provide nonparametric (NPEM) population pharmacokinetic modeling, BOXES for compartments and arrows for pathways, to make large kinetic and dynamic models, and clinical software for Bayesian adaptive control of drug dosage regimens, with D-optimal sampling strategies and explicit determination of assay error patterns.

Bayes Theorem↗
PubMed2003

Neural representation of probabilistic information.

It has been proposed that populations of neurons process information in terms of probability density functions (PDFs) of analog variables. Such analog variables range, for example, from target luminance and depth on the sensory interface to eye position and joint angles on the motor output side. The requirement that analog variables must be processed leads inevitably to a probabilistic description, while the limited precision and lifetime of the neuronal processing units lead naturally to a population representation of information. We show how a time-dependent probability density rho(x; t) over variable x, residing in a specified function space of dimension D, may be decoded from the neuronal activities in a population as a linear combination of certain decoding functions phi(i)(x), with coefficients given by the N firing rates a(i)(t) (generally with D << N). We show how the neuronal encoding process may be described by projecting a set of complementary encoding functions phi;(i)(x) on the probability density rho(x; t), and passing the result through a rectifying nonlinear activation function. We show how both encoders phi;(i)(x) and decoders phi(i)(x) may be determined by minimizing cost functions that quantify the inaccuracy of the representation. Expressing a given computation in terms of manipulation and transformation of probabilities, we show how this representation leads to a neural circuit that can carry out the required computation within a consistent Bayesian framework, with the synaptic weights being explicitly generated in terms of encoders, decoders, conditional probabilities, and priors.

Models, Neurological↗
PubMed1995

Statistical methods for polyploid radiation hybrid mapping.

Radiation hybrid mapping is a somatic cell technique for ordering genetic loci along a chromosome and estimating physical distances between adjacent loci. This paper presents a model of fragment generation and retention for data involving two or more copies of the chromosome of interest per clone. Such polyploid data can be generated by initially irradiating normal diploid cells or by pooling haploid or diploid clones. The current model assumes that fragments are generated in the ancestral cell of a clone according to an independent Poisson breakage process along each chromosome. Once generated, fragments are independently retained in the clone with a common retention probability. On the basis of this and less restrictive retention models, statistical criteria such as minimum obligate breaks, maximum likelihood ratios, and Bayesian posterior probabilities can be used to decide locus order. Distances can be estimated by maximum likelihood. Likelihood computation is particularly challenging, and computing techniques from the theory of hidden Markov chains prove crucial. Within this context it is possible to incorporate typing errors. The statistical tools discussed here are applied to 14 loci on the short arm of human chromosome 4.

Chromosome Mapping↗
PubMed2026

TreeFlow: Probabilistic Modelling and Automatic Differentiation for Phylogenetics.

Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic modelling and automatic differentiation with phylogenetic trees. It embeds phylogenetic trees in the TensorFlow Probability framework, and implements inference algorithms for phylogenetic models given a fixed tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

Bayesian inference↗
PubMed2005

A multiple-feature framework for modelling and predicting transcription factor binding sites.

MOTIVATION: The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcriptional regulation of genes. For analysing transcriptional regulation, computational approaches for predicting putative binding sites are applied. Commonly used stochastic models for binding sites are position-specific score matrices, which show weak predictive power. RESULTS: We have developed a probabilistic modelling approach, which allows to consider diverse characteristic binding site properties to obtain more accurate representations of binding sites. These properties are modelled as random variables in Bayesian networks, which are capable of dealing with dependencies among binding site properties. Cross-validation on several datasets shows improvements in the false positive error rate and the significance (P-value) of true binding sites.

Algorithms↗
PubMed1999

MDL and the statistical mechanics of protein potentials.

The combination of a wealth of structural data and impressive computational power provides detailed information pertaining to the structure and dynamics of biomacromolecules. A natural inclination is to incorporate this information into models to gain added predictive power on protein folding and stability. There has been considerable recent interest in developing "knowledge-based" potentials to describe internal interactions in proteins. In these approaches, probability distribution functions are inferred from existing knowledge. A common assumption has been the "quasi-chemical approximation" or "Boltzmann device". This method relates statistical mechanical probabilities to observed frequencies. The validity of this approach is discussed in detail from a statistical mechanics perspective. Because statistical mechanics is a form of statistical inference based on a lack of knowledge of the system, the "Boltzmann device" does not have a rigorous theoretical justification. In the present work, a statistical mechanics based on partial knowledge of the system is employed. This statistical mechanical scheme uses the minimum description length (MDL) of phase space as its main tool. With this approach, "knowledge-based" potentials can be derived in a rigorous fashion. In practical calculations, these potentials are best obtained using Bayesian inference methods similar to those used in image reconstruction.

Algorithms↗
PubMed2000

Linkage problem, distribution estimation, and Bayesian networks.

This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization Algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.

Algorithms↗
PubMed1992

The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data.

Accurate estimation of the phases and amplitude of the endogenous circadian pacemaker from constant-routine core-temperature series is crucial for making inferences about the properties of the human biological clock from data collected under this protocol. This paper presents a set of statistical methods based on a harmonic-regression-plus-correlated-noise model for estimating the phases and the amplitude of the endogenous circadian pacemaker from constant-routine core-temperature data. The methods include a Bayesian Monte Carlo procedure for computing the uncertainty in these circadian functions. We illustrate the techniques with a detailed study of a single subject's core-temperature series and describe their relationship to other statistical methods for circadian data analysis. In our laboratory, these methods have been successfully used to analyze more than 300 constant routines and provide a highly reliable means of extracting phase and amplitude information from core-temperature data.

Adult↗
PubMed1992

Generalized linear models with random effects; salamander mating revisited.

In recent years much effort has been devoted to extending regression methodology to non-Gaussian data, where responses are not independent. These methods for dependent responses are suitable for data from longitudinal studies or nested designs. However, use of these methods for crossed designs seems to have serious limitations due to the intensive computations involved because of the intractable nature of the joint distribution. In this paper, we cast the problem in a Bayesian framework and use a Monte Carlo method, the Gibbs sampler, to avoid current computational limitations. The flexibility of this approach is illustrated by analyzing the interesting salamander mating data reported by McCullagh and Nelder (1989, Generalized Linear Models, 2nd edition, London: Chapman and Hall).

Analysis of Variance↗
PubMed2000

Using Bayesian networks to analyze expression data.

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998).

Algorithms↗