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 847 records · Page 47Linked to original sources
PubMed2004

Computerized tongue diagnosis based on Bayesian networks.

Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited-application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.

Artificial Intelligence↗
PubMed2004

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Algorithms↗
PubMed2002

Sample size calculation for a historically controlled clinical trial with adjustment for covariates.

We present a Bayesian approach to determining the optimal sample size for a historically controlled clinical trial. This work is motivated by a trial of a new coronary stent that uses a retrospective control group formed from seven trials of coronary stents currently marketed in the United States. In studies involving nonrandomized control groups, hierarchical regression, propensity score methods, or other sophisticated models are typically required to account for heterogeneity among groups which, if ignored could bias the results. Sample size calculations for historically controlled trials of medical devices are often based on formulae derived for randomized trials and fail to account for estimation of model parameters, correlation of observations, and uncertainty in the distribution of covariates of the patients recruited in the new trial. We propose methodology based on stochastic optimization that overcomes these deficiencies. The methodology is demonstrated using an objective function based on the power of the trial from a Bayesian approach. Analytic approximations based on a covariate-free analysis that convey features of the power function are developed. Our principle conclusions are that exact sample size calculations can be substantially different from current approximations, and stochastic optimization provides a convenient method of computation.

Bayes Theorem↗
PubMed2001

Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction.

We apply a generalized Bayesian age-period-cohort (APC) model to a data-set on lung cancer mortality in West Germany, in the period 1952-1996. Our goal is to predict future death rates until the year 2010, separately for males and females. Since age and period are not measured on the same grid, we propose a generalized APC model where consecutive cohort parameters represent strongly overlapping birth cohorts. This approach results in a rather large number of parameters, where standard algorithms for statistical inference by Markov chain Monte Carlo methods turn out to be computationally intensive. We propose a more efficient implementation based on ideas of block sampling from the time series literature. We entertain two different formulations, penalizing either first or second differences of age, period and cohort parameters. To assess the predictive quality of both formulations, we first forecast the rates for the period 1987-1996 based on data until 1986. A comparison with the actual observed rates is made based on a predictive deviance criterion. Predictions of lung cancer mortality until 2010 are then reported and a modification of the formulation in order to include information on cigarette consumption is finally described.To whom correspondence should be addressed. Currently at Imperial College School of Medicine, Department of Epidemiology and Public Health, Norfolk Place, London W2 1PG, UK.

Journal Article↗
PubMed2005

A comparison of two ELISAs for the detection of antibodies to bovine leucosis virus in bulk-milk.

OBJECTIVE: To estimate the sensitivity, specificity and detection limits for two bulk-milk enzyme-linked immunosorbent assays, the Svanovir BLV-gp51-Ab and the Lactelisa BLV Ab Bi indirect tank 250, for the detection of antibody to bovine leucosis virus in milk. PROCEDURE: Milk samples from 27 cows known to have enzootic bovine leucosis (EBL) were serially diluted with milk from a herd known to be free from the disease. The dilution at which antibodies could no longer be detected by each test was determined. A total of 1959 bulk-milk samples submitted to a laboratory for the Victorian (EBL) eradication program were tested with both the Svanovir and the Lactelisa assays. A Bayesian approach was used to calculate maximum-likelihood estimates of test sensitivity and specificity. An additional 660 bulk-milk samples were tested with both the Svanovir and the Lactelisa assays. Herds that had positive results on either or both of the assays were subjected to blood or milk testing of individual cattle. RESULTS: The dilution of milk at which the Svanovir assay failed to detect enzootic bovine leucosis antibody in half of the samples was 1 in 40, whereas the comparable value for the Lactelisa was 1 in 200. Computer modeling of the operating characteristics of the Svanovir assay indicated that the sensitivity of that assay would be considerably lower than that for the Lactelisa, and the specificity was estimated to be higher. Evaluation of the assays using 660 bulk-milk samples showed that the Lactelisa assay detected four infected herds that were not detected by the Svanovir test. No false positive results were recorded for either assay. CONCLUSION: Use of the Lactelisa assay in the Victorian EBL eradication program will enhance disease detection and eradication, but may also result in an increased frequency of false positive bulk-milk test results.

Animals↗
PubMed2004

Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference.

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.

Adolescent↗
PubMed2006

Non-Gaussian velocity distributions integrated over space, time, and scales.

Velocity distributions are an enhanced representation of image velocity containing more velocity information than velocity vectors. In particular, non-Gaussian velocity distributions allow for the representation of ambiguous motion information caused by the aperture problem or multiple motions at motion boundaries. To resolve motion ambiguities, discrete non-Gaussian velocity distributions are suggested, which are integrated over space, time, and scales using a joint Bayesian prediction and refinement approach. This leads to a hierarchical velocity-distribution representation from which robust velocity estimates for both slow and high speeds as well as statistical confidence measures rating the velocity estimates can be computed.

Algorithms↗
PubMed2004

Testing nested phylogenetic and phylogeographic hypotheses in the Plethodon vandykei species group.

Mesic forests in the North American Pacific Northwest occur in two disjunct areas: along the coastal and Cascade ranges of Oregon, Washington, and British Columbia as well as the Northern Rocky Mountains of Idaho, Montana, and British Columbia. Over 150 species or species complexes have disjunct populations in each area, and a priori hypotheses based on phytogeography and geology potentially explain the disjunction via either dispersal or vicariance. Here, we test these hypotheses in the disjunct salamander complex Plethodon vandykei and P. idahoensisby collecting genetic data (669 bp of Cyt b) from 262 individuals. Maximum likelihood analysis indicated reciprocal monophyly of these species, supporting the ancient vicariance hypothesis, whereas parametric bootstrap and Bayesian hypothesis testing allow rejection of the dispersal hypothesis. The coalescent estimate of the time since population divergence (estimated using MDIV) is 3.75 x 106 years, and the 95%credibility interval of this value overlaps with the geological estimate of vicariance, but not the hypothesized dispersal. These results are congruent with the pattern seen in other mesic forest amphibian lineages and suggest disjunction in amphibians may be a concerted response to a geological/climatological event. WithinP. idahoensis, we tested the corollary hypothesis of an inland Pleistocene refugium in the Clearwater drainage with nested clade analysis and coalescent estimates of population growth rate (g). Both analyses support post-Pleistocene expansion from the Clearwater refugium. We corroborated this result by calculating Tajima's Dand mismatch distribution within each drainage, showing strong evidence for recent population expansion within most drainages. This work demonstrates the utility of statistical phylogeography and contributes two novel analytical tools: tests of stationarity with respect to topology in the Bayesian estimation, and the use of coalescent simulations to test the significance of the population growth-rate parameter.

Analysis of Variance↗
PubMed2006

BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.

UNLABELLED: BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant sub-modules of regulatory networks with high confidence are reconstructed by using our extended sub-network mining algorithm of directed graphs. BNArray can handle microarray datasets with missing data. To evaluate the statistical features of generated Bayesian networks, re-sampling procedures are utilized to yield collections of candidate 1st-order network sets for mining dense coherent sub-networks. AVAILABILITY: The R package and the supplementary documentation are available at http://www.cls.zju.edu.cn/binfo/BNArray/.

Algorithms↗
PubMed2006

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Abscisic Acid↗
PubMed2001

Novel Bayesian multiscale method for speckle removal in medical ultrasound images.

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.

Bayes Theorem↗
PubMed2002

Bayesian modeling of incidence and progression of disease from cross-sectional data.

In the absence of longitudinal data, the current presence and severity of disease can be measured for a sample of individuals to investigate factors related to disease incidence and progression. In this article, Bayesian discrete-time stochastic models are developed for inference from cross-sectional data consisting of the age at first diagnosis, the current presence of disease, and one or more surrogates of disease severity. Semiparametric models are used for the age-specific hazards of onset and diagnosis, and a normal underlying variable approach is proposed for modeling of changes with latency time in disease severity. The model accommodates multiple surrogates of disease severity having different measurement scales and heterogeneity among individuals in disease progression. A Markov chain Monte Carlo algorithm is described for posterior computation, and the methods are applied to data from a study of uterine leiomyoma.

Age of Onset↗
PubMed1997

Bayesian design and analysis of two x two factorial clinical trials.

The 2 x 2 factorial design has been advocated for improving the efficiency of clinical trials. Most such trials are designed on the assumption that there is no interaction between the levels of the factors and outcome. This assumption is often problematic, however, because interactions are usually possible in clinical trials and the sample sizes often used provide little power in testing for interactions. We consider the use of Bayesian methods for the design and analysis of 2 x 2 factorial clinical trials. This approach avoids the need to dichotomize one's assumptions that interactions either do or do not exist and provides a flexible approach to the design and analysis of such clinical trials. Exact results are developed for balanced factorial designs with normal response. Approximations are then presented for factorial designs based on the logistic model for binary response or the proportional hazards model for time-to-event data. The resulting approximate posterior distributions are normal and hence no extensive computations are required. Suggestions for specification of prior distributions are presented.

Antineoplastic Agents, Phytogenic↗
PubMed2025

Bayesian classification of OXPHOS deficient skeletal myofibres.

Mitochondria are organelles in most human cells which release the energy required for cells to function. Oxidative phosphorylation (OXPHOS) is a key biochemical process within mitochondria required for energy production and requires a range of proteins and protein complexes. Mitochondria contain multiple copies of their own genome (mtDNA), which codes for some of the proteins and ribonucleic acids required for mitochondrial function and assembly. Pathology arises from genetic defects in mtDNA and can reduce cellular abundance of OXPHOS proteins, affecting mitochondrial function. Due to the continuous turn-over of mtDNA, pathology is random and neighbouring cells can possess different OXPHOS protein abundance. Estimating the proportion of cells where OXPHOS protein abundance is too low to maintain normal function is critical to understanding disease severity and predicting disease progression. Currently, one method to classify single cells as being OXPHOS deficient is prevalent in the literature. The method compares a patient's OXPHOS protein abundance to that of a small number of healthy control subjects. If the patient's cell displays an abundance which differs from the abundance of the controls then it is deemed deficient. However, due to the natural variation between subjects and the low number of control subjects typically available, this method is inflexible and often results in a large proportion of patient cells being misclassified. These misclassifications have significant consequences for the clinical interpretation of these data. We propose a single-cell classification method using a Bayesian hierarchical mixture model, which allows for inter-subject OXPHOS protein abundance variation. The model accurately classifies an example dataset of OXPHOS protein abundances in skeletal muscle fibres (myofibres). When comparing the proposed and existing model classifications to manual classifications performed by experts, the proposed model results in estimates of the proportion of deficient myofibres that are consistent with expert manual classifications.

Oxidative Phosphorylation↗
PubMed2000

Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.

OBJECTIVE: This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge. DESIGN: This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves. RESULTS: The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model. CONCLUSION: Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone.

Artificial Intelligence↗
PubMed2005

Modeling K(m) values using electrotopological state: substrates for cytochrome P450 3A4-mediated metabolism.

In order to determine K(m) values of substrates for CYP3A4-mediated metabolism, an in silico model has been developed in the present work. Using electrotopological state (E-state) indices, together with Bayesian-regularized neural network (BRNN), we have described an in silico method to model log(1/K(m)) values of various substrates. The relative importance of the E-state indices is analyzed by principal component analysis. By using an additional external test set, which is independent of the training set, the robustness and predictivity of the model are also validated.

Computational Biology↗
PubMed2005

Finding associations in dense genetic maps: a genetic algorithm approach.

Large-scale association studies hold promise for discovering the genetic basis of common human disease. These studies will consist of a large number of individuals, as well as large number of genetic markers, such as single nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between phenotypes and SNPs in dense genetic maps. Our approach uses a genetic algorithm (GA) to construct logic trees consisting of Boolean expressions involving strings or blocks of SNPs. These blocks or nodes of the logic trees consist of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other due to evolutionary processes. At each generation of our GA, a population of logic tree models is modified using selection, cross-over and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression frame-work. Mutation and cross-over moves use LD measures to pro pose changes to the trees, and facilitate the movement through the model space. We demonstrate our method and the flexibility of logic tree structure with variable nodal lengths on simulated data from a coalescent model, as well as data from a candidate gene study of quantitative genetic variation.

Algorithms↗
PubMed2002

A comparison of material classification techniques for ultrasound inverse imaging.

The conjugate gradient method with edge preserving regularization (CGEP) is applied to the ultrasound inverse scattering problem for the early detection of breast tumors. To accelerate image reconstruction, several different pattern classification schemes are introduced into the CGEP algorithm. These classification techniques are compared for a full-sized, two-dimensional breast model. One of these techniques uses two parameters, the sound speed and attenuation, simultaneously to perform classification based on a Bayesian classifier and is called bivariate material classification (BMC). The other two techniques, presented in earlier work, are univariate material classification (UMC) and neural network (NN) classification. BMC is an extension of UMC, the latter using attenuation alone to perform classification, and NN classification uses a neural network. Both noiseless and noisy cases are considered. For the noiseless case, numerical simulations show that the CGEP-BMC method requires 40% fewer iterations than the CGEP method, and the CGEP-NN method requires 55% fewer. The CGEP-BMC and CGEP-NN methods yield more accurate reconstructions than the CGEP method. A quantitative comparison of the CGEP-BMC, CGEP-NN, and GN-UMC methods shows that the CGEP-BMC and CGEP-NN methods are more robust to noise than the GN-UMC method, while all three are similar in computational complexity.

Breast Neoplasms↗