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PubMed2006

Bayesian approach for quantifying the uncertainty of neutron doses derived from spectrometric measurements.

Bayesian methods provide a unified framework for combining information in the presence of uncertainty. All uncertainties that enter into the description of a measurement are modelled using probability distributions, and these are handled according to the rules of probability theory, ensuring that the approach is free of inconsistencies. The final result of the analysis is the full probability distribution for the parameter of interest, and from this distribution an appropriate uncertainty interval can be obtained. Some of the advantages of a Bayesian analysis include a straightforward approach to the problem of dealing with nuisance parameters, the ability to incorporate prior information in a natural way and the flexibility that is necessary for a realistic modelling of the measurement process. As an example, the problem of deriving neutron dose estimates and their uncertainties based on measurements carried out using a Bonner sphere spectrometer is considered.

Bayes Theorem↗

PubMed2006

Development and Bayesian evaluation of an ELISA to detect specific antibodies to Sarcoptes scabiei var suis in the meat juice of pigs.

Samples of ear scrapings, serum and diaphragmatic muscle were collected from 271 fattening pigs at the slaughterhouse. The scrapings were examined for the presence of mites, and tests for specific antibodies to Sarcoptes scabiei var suis in the serum and meat juice were made with an experimental ELISA. The cut-off value for the meat-juice ELISA was estimated at an optical density of 0.5 by receiver operating characteristic curve analysis, on the basis of the cut-off value for the serum ELISA of 0.4. The results of the three tests were used in a Bayesian model to estimate the characteristics of each test. The specificity of the tests of the ear scrapings was considered to be 1 and their sensitivity was estimated by Bayesian analysis to be 0.86, with a 95 per cent confidence interval (CI) of 0.73 to 0.99. The sensitivity of the meat juice ELISA (0.71, 95 per cent CI 0.6 to 0.8) and its specificity (0.77, 95 per cent CI 0.66 to 0.89) were comparable with the sensitivity (0.73, 95 per cent CI 0.6 to 0.8) and specificity (0.81, 95 per cent CI 0.69 to 0.95) of the serum ELISA.

Abattoirs↗

PubMed2004

BAPS 2: enhanced possibilities for the analysis of genetic population structure.

UNLABELLED: Bayesian statistical methods based on simulation techniques have recently been shown to provide powerful tools for the analysis of genetic population structure. We have previously developed a Markov chain Monte Carlo (MCMC) algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset. However, for large-scale datasets such algorithms may get stuck to local maxima in the parameter space. Therefore, we have modified our earlier algorithm to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm. We consider also a hierarchical tree representation, from which a Bayesian model-averaged structure estimate can be extracted. The algorithm is implemented in a computer program that features a user-friendly interface and built-in graphics. The enhanced features are illustrated by analyses of simulated data and an extensive human molecular dataset. AVAILABILITY: Freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.

Algorithms↗

PubMed2006

Multi-locus phylogeny of dolphins in the subfamily Lissodelphininae: character synergy improves phylogenetic resolution.

BACKGROUND: Dolphins of the genus Lagenorhynchus are anti-tropically distributed in temperate to cool waters. Phylogenetic analyses of cytochrome b sequences have suggested that the genus is polyphyletic; however, many relationships were poorly resolved. In this study, we present a combined-analysis phylogenetic hypothesis for Lagenorhynchus and members of the subfamily Lissodelphininae, which is derived from two nuclear and two mitochondrial data sets and the addition of 34 individuals representing 9 species. In addition, we characterize with parsimony and Bayesian analyses the phylogenetic utility and interaction of characters with statistical measures, including the utility of highly consistent (non-homoplasious) characters as a conservative measure of phylogenetic robustness. We also explore the effects of removing sources of character conflict on phylogenetic resolution. RESULTS: Overall, our study provides strong support for the monophyly of the subfamily Lissodelphininae and the polyphyly of the genus Lagenorhynchus. In addition, the simultaneous parsimony analysis resolved and/or improved resolution for 12 nodes including: (1) L. albirostris, L. acutus; (2) L. obscurus and L. obliquidens; and (3) L. cruciger and L. australis. In addition, the Bayesian analysis supported the monophyly of the Cephalorhynchus, and resolved ambiguities regarding the relationship of L. australis/L. cruciger to other members of the genus Lagenorhynchus. The frequency of highly consistent characters varied among data partitions, but the rate of evolution was consistent within data partitions. Although the control region was the greatest source of character conflict, removal of this data partition impeded phylogenetic resolution. CONCLUSION: The simultaneous analysis approach produced a more robust phylogenetic hypothesis for Lagenorhynchus than previous studies, thus supporting a phylogenetic approach employing multiple data partitions that vary in overall rate of evolution. Even in cases where there was apparent conflict among characters, our data suggest a synergistic interaction in the simultaneous analysis, and speak against a priori exclusion of data because of potential conflicts, primarily because phylogenetic results can be less robust. For example, the removal of the control region, the putative source of character conflict, produced spurious results with inconsistencies among and within topologies from parsimony and Bayesian analyses.

Actins↗

PubMedDate not supplied

Interim analyses in clinical trials: classical vs. Bayesian approaches.

This paper concerns interim analysis in clinical trials involving two treatments from the points of view of both classical and Bayesian inference. I criticize classical hypothesis testing in this setting and describe and recommend a Bayesian approach in which sampling stops when the probability that one treatment is the better exceeds a specified value. I consider application to normal sampling analysed in stages and evaluate the gain in average sample number as a function of the number of interim analyses.

Bayes Theorem↗

PubMed2006

A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates.

BACKGROUND: Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high. RESULTS: We describe a statistical procedure for performing differential expression analysis without replicates. The procedure relies on a Bayesian integrated approach (BGX) to the analysis of Affymetrix GeneChips. The BGX method estimates a posterior distribution of expression for each gene and condition, from a simultaneous consideration of the available probe intensities representing the gene in a condition. Importantly, posterior distributions of expression are obtained regardless of the number of replicates available. We exploit these posterior distributions to create ranked gene lists that take into account the estimated expression difference as well as its associated uncertainty. We estimate the proportion of non-differentially expressed genes empirically, allowing an informed choice of cut-off for the ranked gene list, adapting an approach proposed by Efron. We assess the performance of the method, and compare it to those of other methods, on publicly available spike-in data sets, as well as in a proper biological setting. CONCLUSION: The method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates.

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↗

PubMed2004

The current status of the small subunit rRNA phylogeny of the coccidia (Sporozoa).

There is no current comprehensive assessment of the molecular phylogeny of the coccidia, as all recently published papers either deal with subsets of the taxa or sequence data, or provide non-robust analyses. Here, we present a comprehensive and consistent phylogenetic analysis of the available data for the small-subunit ribosomal RNA gene sequence, including a number of taxa not previously studied, based on a Bayesian tree-building analysis and the covariotide model of evolution. The assumptions of the analysis have been rigorously tested, and the benefits and limitations highlighted. Our results provide support for a number of prior conclusions, including the monophyly of the families Sarcocystidae (cyst-forming coccidia) and Eimeriidae (oocyst-forming coccidia), but with bird-host Isospora species in the Eimeriidae and mammal-host species in the Sarcocystidae. However, it is clear that a number of previously reported relationships are dependent on the evolutionary model chosen, such as the placements of Goussia janae, Lankesterella minimia and Caryospora bigenetica. Our results also confirm the monophyly of the subfamilies Toxoplasmatinae and Sarcocystinae, but only some of the previously reported groups within these subfamilies are supported by our analysis. Similarly, only some of the previously reported groups within the Eimeriidae are supported by our analysis, and the genus Eimeria is clearly paraphyletic. There are unambiguous patterns of host-parasite relationship within the coccidia, as most of the well-supported groups have a consistent and restricted range of hosts, with the exception of the Toxoplasmatinae. Furthermore, the previously reported groups for which we found no support all have a diverse range of unrelated hosts, confirming that these are unlikely to be natural groups. The most interesting unaddressed questions may relate to Isospora, which has the fewest available sequences and host-parasite relationships apparently not as straightforward as elsewhere within the suborder.

Animals↗

PubMed2007

A Bayesian population PK-PD model of ispinesib-induced myelosuppression.

The goal of the present analysis is to fit a Bayesian population pharmacokinetic pharmacodynomic (PK-PD) model to characterize the relationship between the concentration of ispinesib and changes in absolute neutrophil counts (ANC). Ispinesib, a kinesin spindle protein (KSP) inhibitor, blocks assembly of a functional mitotic spindle, leading to G2/M arrest. A first time in human, phase I open-label, non-randomized, dose-escalating study evaluated ispinesib at doses ranging from 1 to 21 mg/m(2). PK-PD data were collected from 45 patients with solid tumors. The pharmacokinetics of ispinesib were well characterized by a two-compartment model. A semimechanistic model was fit to the ANC. The PK and PD data were successfully modelled simultaneously. This is the first presentation of simultaneously fitting a PK-PD model to ANC using Bayesian methods. Bayesian methods allow for the use of prior information for some system-related parameters. The model may be used to examine different schedules, doses, and infusion times.

Adult↗

PubMed2005

Bayesian network learning with feature abstraction for gene-drug dependency analysis.

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

Algorithms↗

PubMed2005

A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method.

Common human disorders, such as alcoholism, may be the result of interactions of many genes as well as environmental risk factors. Therefore, it is important to incorporate gene x gene and gene x environment interactions in complex disease gene mapping. In this study, we applied a robust Bayesian genome screening method that can incorporate interaction effects to map genes underlying alcoholism through its application to the data of the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. Our Bayesian genome screening method uses the regression-based stochastic variable selection, coupled with the new Haseman-Elston method to identify markers linked to phenotypes of interest. Compared to traditional linkage methods based on single-gene disease models, our method allows for multilocus disease models for simultaneous screening including both main and interaction (epistatic) effects. It is conceptually simple and computationally efficient through the use of Gibbs sampler. We conducted genome-wide analysis and comparison between scans based on microsatellites and single-nucleotide polymorphisms. A total of 328 microsatellites and 11,560 single-nucleotide polymorphisms (by Affymetrix) on 22 autosomal chromosomes and sex chromosome were used.

Alcoholism↗

PubMed2000

Introduction to Bayesian modelling in dental research.

OBJECTIVE: To explain the concepts and application of Bayesian modelling and how it can be applied to the analysis of dental research data. BASIC DESIGN: Methodological in nature, this article introduces Bayesian modelling through hypothetical dental examples. SETTING: The synthesis of RCT results with previous evidence, including expert opinion, is used to illustrate full Bayesian modelling. Meta-analysis, in the form of empirical Bayesian modelling, is introduced. An example of full Bayesian modelling is described for the synthesis of evidence from several studies that investigate the success of root canal treatment. Hierarchical (Bayesian) modelling is demonstrated for a survey of childhood caries, where surface data is nested within subjects. RESULTS: Bayesian methods enhance interpretation of research evidence through the synthesis of information from multiple sources. CONCLUSIONS: Bayesian modelling is now readily accessible to clinical researchers and is able to augment the application of clinical decision making in the development of guidelines and clinical practice.

Bayes Theorem↗

PubMed2004

Phylogenetic analysis of the full-length SARS-CoV sequences: evidence for phylogenetic discordance in three genomic regions.

The origin of the severe acute respiratory syndrome-coronavirus (SARS-CoV) remains unclear. Evidence based on Bayesian scanning plots and phylogenetic analysis using maximum likelihood (ML) and Bayesian methods indicates that SARS-CoV, for the largest part of the genome ( approximately 80%), is more closely related to Group II coronaviruses sequences, whereas in three regions in the ORF1ab gene it shows no apparent similarity to any of the previously characterized groups of coronaviruses. There is discordant phylogenetic clustering of SARS-CoV and coronaviruses sequences, throughout the genome, compatible with either ancient recombination events or altered evolutionary rates in different lineages, or a combination of both.

Evolution, Molecular↗

PubMed2006

Evaluation of decay times in coupled spaces: an efficient search algorithm within the Bayesian framework.

This paper discusses an efficient method for evaluating multiple decay times within the Bayesian framework. Previous works [N. Xiang and P. M. Goggans, J. Acoust. Soc. Am. 110, 1415-1424 (2001); 113, 2685-2697 (2003); N. Xiang, P. M. Goggans, T. Jasa, and M. Kleiner, 117, 3707-3715 (2005)] have applied the Bayesian inference to cope with demanding tasks in estimating multiple decay times from Schroeder decay functions measured or calculated in acoustically coupled spaces. Since then a number of recent works call for efficient estimation methods within the Bayesian framework. An efficient analysis is of practical significance for better understanding and modeling the sound energy decay process in acoustically coupled spaces or even in single spaces for reverberation time estimation. This paper will first formulate the Bayesian posterior probability distribution function (PPDF) in a matrix form to reduce the dimensionality as applied to the decay time evaluation. Based on existence of only global extremes of PPDFs as observed from extensive experimental data, this paper describes a dedicated search algorithm for an efficient estimation of decay times.

Journal Article↗

PubMed2004

Evaluation of HIV-1 kinetic models using quantitative discrimination analysis.

MOTIVATION: Since the identification of human immunodeficiency virus (HIV) over twenty years ago, many mathematical models of HIV dynamics have been proposed. The purpose of this study was to evaluate intracellular and intercellular scale HIV models that best described the dynamics of viral and cell titers of a person, where parameters were determined using typically available patient data. In this case, 'best' was defined as the model most capable of describing experimental patient data and was determined by Bayesian-based model discrimination analysis and the ability to provide realistic results. RESULTS: Twenty models of HIV-1 viral dynamics were initially evaluated to determine whether parameters could be obtained from readily available clinical data from established HIV-1 patients with stable disease. Based on this analysis, three models were chosen for further examination and comparison. Parameters were estimated using experimental data from a cohort of 338 people monitored for up to 2484 days. The models were evaluated using a Bayesian technique to determine which model was most probable. The model ultimately selected as most probable was overwhelmingly favored relative to the remaining two models, and it accounted for uninfected cells, infected cells and cytotoxic T lymphocyte dynamics. The authors developed a fourth model for comparison purposes by combining the features of the original three models. Parameters were estimated for the new model and the statistical analysis was repeated for all four models. The model that was initially favored was selected again upon model discrimination analysis. CONTACT: srivasta@engr.uconn.edu.

Cohort Studies↗

PubMed1982

The contributions of Jerome Cornfield to the theory of statistics.

This paper is a review of the contributions of Jerome Cornfield to the theory of statistics. It discusses several highlights of his theoretical work as well as describing his philosophy relating theory to application. The three areas discussed are: linear programming, urn sampling and its generalizations to the analysis of variance, and Bayesian inference. It is not widely known that Jerome Cornfield was perhaps the first to formulate and approximately solve the linear programming problem in 1941. His formulation was made for the famous "Diet Problem". An early publication introduced the method of indicator random variables in the context of urn sampling. This simple method allowed straightforward calculations of the low order moments for estimates arising from sampling finite populations and was later generalized to the two-way analysis of variance. The application of the urn sampling model to the analysis of variance served to illuminate how one chooses proper error terms for making tests in the analysis of variance table. Jerome Cornfield's philosophy on applications of statistics was dominated by a Bayesian outlook. His theoretical contributions in the past two decades were mainly concerned with the development of Bayesian ideas and methods. A brief survey is made of his main contributions to this area. A particularly noteworthy result was his demonstration that for the two-sample slippage problem of location, the likelihood function under a permutation setting is uninformative for the slippage parameter. However, the posterior distribution differs from the prior distribution despite the fact that the likelihood is uninformative.

Bayes Theorem↗

PubMed2004

Molecular phylogenetics and historical biogeography of Hawaiian Dryopteris (Dryopteridaceae).

The fern genus Dryopteris (Dryopteridaceae) is represented in the Hawaiian Islands by 18 endemic taxa and one non-endemic, native species. The goals of this study were to determine whether Dryopteris in Hawai'i is monophyletic and to infer the biogeographical origins of Hawaiian Dryopteris by determining the geographical distributions of their closest living relatives. We sequenced two chloroplast DNA fragments, rbcL and the trnL-F intergenic spacer (IGS), for 18 Hawaiian taxa, 45 non-Hawaiian taxa, and two outgroup species. For individual fragments, we estimated phylogenetic relationships using Bayesian inference and maximum parsimony. We performed a combined analysis of both cpDNA fragments employing Bayesian inference, maximum parsimony, and maximum likelihood. These analyses indicate that Hawaiian Dryopteris is not monophyletic, and that there were at least five separate colonizations of the Hawaiian Islands by different species of dryopteroid ferns, with most of the five groups having closest relatives in SE Asia. The results suggest that one colonizing ancestor, perhaps from SE Asia, gave rise to eight endemic taxa (the glabra group). Another colonizing ancestor, also possibly from SE Asia, gave rise to a group of five endemic taxa (the exindusiate group). Dryopteris fusco-atra and its two varieties, which are endemic to Hawai'i, most likely diversified from a SE Asian ancestor. The Hawaiian endemic Nothoperanema rubiginosum has its closest relatives in SE Asia, and while the remaining two species, D. wallichiana and D. subbipinnata, are sister species, their biogeographical origins could not be determined from these analyses due to the widespread distributions of D. wallichiana and its closest non-Hawaiian relative.

Bayes Theorem↗

PubMed2005

An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding.

Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the Bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.

Algorithms↗