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PubMed2004

Model-free analysis of protein dynamics: assessment of accuracy and model selection protocols based on molecular dynamics simulation.

The popular model-free approach to analyze NMR relaxation measurements has been examined using artificial amide (15)N relaxation data sets generated from a 10 nanosecond molecular dynamics trajectory of a dihydrofolate reductase ternary complex in explicit water. With access to a detailed picture of the underlying internal motions, the efficacy of model-free analysis and impact of model selection protocols on the interpretation of NMR data can be studied. In the limit of uncorrelated global tumbling and internal motions, fitting the relaxation data to the model-free models can recover a significant amount of quantitative information on the internal dynamics. Despite a slight overestimation, the generalized order parameter is quite accurately determined. However, the model-free analysis appears to be insensitive to the presence of nanosecond time scale motions with relatively small magnitude. For such cases, the effective correlation time can be significantly underestimated. As a result, proteins appear to be more rigid than they really are. The model selection protocols have a major impact on the information one can reliably obtain. The commonly employed protocol based on step-up hypothesis testing has severe drawbacks of oversimplification and underfitting. The consequences are that the order parameter is more severely overestimated and the correlation time more severely underestimated. Instead, model selection based on Bayesian Information Criteria (BIC), recently introduced to the model-free analysis by d'Auvergne and Gooley (2003), provides a better balance between bias and variance. More appropriate models can be selected, leading to improved estimate of both the order parameter and correlation time. In addition, the computational cost is significantly reduced and subjective parameters such as the significance level are unnecessary.

Anisotropy↗
PubMed1997

Bayesian color constancy.

The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor responses. Second, we construct prior distributions that describe the probability that particular illuminants and surfaces exist in the world. Given a set of photosensor responses, we can then use Bayes's rule to compute the posterior distribution for the illuminants and the surfaces in the scene. There are two widely used methods for obtaining a single best estimate from a posterior distribution. These are maximum a posteriori (MAP) and minimum mean-square-error (MMSE) estimation. We argue that neither is appropriate for perception problems. We describe a new estimator, which we call the maximum local mass (MLM) estimate, that integrates local probability density. The new method uses an optimality criterion that is appropriate for perception tasks: It finds the most probable approximately correct answer. For the case of low observation noise, we provide an efficient approximation. We develop the MLM estimator for the color-constancy problem in which flat matte surfaces are uniformly illuminated. In simulations we show that the MLM method performs better than the MAP estimator and better than a number of standard color-constancy algorithms. We note conditions under which even the optimal estimator produces poor estimates: when the spectral properties of the surfaces in the scene are biased.

Color Perception↗
PubMed1995

Adaptive control of therapeutic drug regimens relations between clinical situations: outcomes and simulations using nonlinear dynamic models.

With Bayesian modeling and adaptive control of drug dosage regimens, serum and peripheral drug concentrations can be predicted in clinical situations using linear pharmacokinetic compartmental models (PK). Recently, several pathophysiologic and pharmacodynamic nonlinear models (PD) have been developed. The present report illustrates both their utility and limits for the computation of effects in clinical situations in the setting of actual routine and acute patient care. Patients who received therapy with aminoglycosides or/and vancomycin were selected. For each patient, after estimation of individual pharmacokinetic parameters, the computed outputs of the linear compartmental pharmacokinetic model were used as inputs for 2 different a priori nonlinear dynamic models: 1) the EFFECT modeling program, using a Hill model, and 2) the BACTCIDE program, which is a combination of a simple growth model for the organism and a Hill effect model considering both the microorganism, the antibiotic, and the patient's minimal inhibitory concentration (MIC). The programs (1) and (2) can use as inputs the computed concentrations from any of three compartments: central, peripheral, or a spherical diffusion compartment to compute drug diffusion into endocardial vegetations or abscesses. The EFFECT program can be used alone for the evaluation of drug effects. The BACTCIDE program illustrates differences in activity between concentration-dependent and time-dependent antibiotics. Such nonlinear programs are very sensitive to the MIC values.

Aged↗
PubMed2006

Predicting postoperative nausea and vomiting with the application of an artificial neural network.

BACKGROUND: Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS: We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia without antiemetic prophylaxis. Predictors used for ANN training were selected by computing the value of chi(2) statistic and information gain with respect to PONV. The configuration of the ANN was chosen by using a software tool. Then the training of the ANN was performed based on data from a training set (n=656). Testing validation was performed with the remaining patients (n=430) whose outcome regarding PONV was unknown to the ANN. Area under the receiver operating characteristic (ROC) curves were used to quantify predictive performance. ANN performance was compared with those of the Naïve Bayesian classifier model, logistic regression model, simplified Apfel score and Koivuranta score. RESULTS: ANN accuracy was 83.3%, sensitivity 77.9% and specificity 85.0% in predicting PONV. The areas under the ROC curve follow: ANN, 0.814 (0.774-0.850); Naïve Bayesian classifier, 0.570 (0.522-0.617); logistic regression, 0.669 (0.623-0.714); Koivuranta score, 0.626 (0.578-0.672); simplified Apfel score, 0.624 (0.576-0.670). ANN discriminatory power was superior to those of the other predicting models (P<0.05). CONCLUSIONS: The ANN provided the best predictive performance among all tested models.

Adult↗
PubMed2002

Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.

MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

Cell Cycle↗
PubMed2006

Two-view multibody structure-and-motion with outliers through model selection.

Multibody structure-and-motion (MSaM) is the problem to establish the multiple-view geometry of several views of a 3D scene taken at different times, where the scene consists of multiple rigid objects moving relative to each other. We examine the case of two views. The setting is the following: Given are a set of corresponding image points in two images, which originate from an unknown number of moving scene objects, each giving rise to a motion model. Furthermore, the measurement noise is unknown, and there are a number of gross errors, which are outliers to all models. The task is to find an optimal set of motion models for the measurements. It is solved through Monte-Carlo sampling, careful statistical analysis of the sampled set of motion models, and simultaneous selection of multiple motion models to best explain the measurements. The framework is not restricted to any particular model selection mechanism because it is developed from a Bayesian viewpoint: Different model selection criteria are seen as different priors for the set of moving objects, which allow one to bias the selection procedure for different purposes.

Algorithms↗
PubMed2006

One-shot learning of object categories.

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.

Algorithms↗
PubMed1992

Predictive performance of Bayesian and nonlinear least-squares regression programs for lidocaine.

The predictive performance of two computer programs for lidocaine dosing were evaluated. Two-compartment Bayesian and nonlinear least-squares regression programs were used in two groups of patients (15 acute arrhythmia patients and 14 chronic arrhythmia patients). Lidocaine was given as a 1.5 mg/kg bolus and a 2.8 mg/min infusion for 48 h. A second bolus (0.5 mg/kg) was given 10 min after the first bolus over 2 min. Serum samples of the patients receiving lidocaine were drawn at 2, 15, 30 min and 1, 2, and 4 h and were used in forecasting the serum concentrations at 6, 8, 12, and 48 h. Predictive performance was assessed by mean error and mean-squared error. The results (mean +/- 95% confidence intervals) demonstrated the Bayesian program predicted a significant (p less than 0.05) difference at 12 h between the two arrhythmia groups (acute 0.52 [-0.95; -0.09] and chronic 0.28 [0.12; 0.44]). The results also demonstrated the Bayesian method was significantly more precise compared to the nonlinear least-squares regression program at 8, 12, and 48 h for the acute group. While caution is warranted, this study demonstrated that the predictive performance by a two-compartment Bayesian model is more accurate in predicting future lidocaine serum concentrations than that by nonlinear least-squares regression.

Acute Disease↗
PubMed2005

Predicting the usefulness of therapeutic drug monitoring of mycophenolic acid: a computer simulation.

The usefulness of therapeutic drug monitoring (TDM) of mycophenolate mofetil (MMF) was investigated with a computer simulation model. For a fixed-dose (FD) and a concentration-controlled (CC) MMF dosing regimen exposure to mycophenolic acid (MPA) was compared. A nonlinear mixed-effects model (NONMEM) for MPA based on extensive pharmacokinetic data from 140 renal transplant recipients who all used cyclosporine and corticosteroids as maintenance immunosuppressive therapy provided Bayesian estimates for MPA oral clearance on 9 occasions during the first 24 weeks after transplantation. In 45 of these patients, the estimates for MPA oral clearance were used to calculate values for the area under the curve (AUC) of MPA. In the CC group, MMF doses were adjusted based on the calculated AUC, targeting at an AUC level of 45 mg.h/L. In the FD group, MMF doses were fixed at 1000 mg. On day 7 after transplantation, significantly more AUC values were on target (AUC range 30-60 mg.h/L) in the CC group than in the FD group: 76% versus 13%, respectively, P < 0.001. To accomplish this, a doubling of MMF dose was necessary in more than half of the patients after the AUC assessment on day 3 after transplantation. Between-patient variability (BPV) in AUC (average CV% for all occasions) was reduced in the CC regimen: 23% versus 44% in the FD group. By using TDM, adequate MPA exposure appears to be obtained more rapidly, and BPV in exposure is reduced. To reach target AUC levels as soon as possible in this cyclosporine-treated population, it appears that larger MMF doses as currently recommended are necessary in the first month after transplantation.

Administration, Oral↗
PubMed1994

Pharmacokinetics and dosage regimens of amikacin in intensive care unit patients.

The pharmacokinetics of amikacin have been studied in 40 intensive care unit (ICU) patients using a two-compartment model and the Bayesian estimation method implemented in the USC PC-PACK program of Jelliffe et al. The volume of the central compartment was significantly higher in these patients (0.36 l.kg-1) than in the reference population (0.20 l.kg-1). A method has been designed to compute dosage regimens in order to maintain a constant steady-state average plasma concentration of 8 mg.l-1 for repeated i.v. infusions. The regimen calculated for the 'average' ICU patient varies between 11 mg.kg-1 three times per day for the patient with normal renal function and 6 mg.kg-1 every 2 days for the anuric patient. This regimen is intended to begin amikacin therapy in an ICU patient, while the population pharmacokinetic parameters would allow the individualization of the regimen by means of the Bayesian method.

Amikacin↗
PubMed2004

The analysis of peak expiratory flow data using a three-level hierarchical model.

Peak expiratory flow (PEF) is a measure commonly used in clinical practice and research for respiratory diseases such as asthma. In research, PEF is usually recorded in a diary for a 2-week period with two or more measurements per day. Interest may lie in whether certain groups of individuals tend to have higher or lower PEF. In addition the variability of PEF may be of interest as, for example, asthmatics tend to have more variable airways. In this paper we develop a three-level hierarchical model that can simultaneously model the mean level and variability of PEF. The variability is broken down into three components, between-subject variability, between-day within-subject variability, and within-day within-subject variability. The latter two components are of specific clinical interest. We fit both classical and Bayesian models. The Bayesian models have the advantage of taking the uncertainty in the variance component estimates into account when estimating the standard errors of the fixed effects. In addition, the Bayesian models provide an intuitive and simple way to investigate the within-subject variance components.

Asthma↗
PubMed2005

Bayesian inferences on predictors of conception probabilities.

Reproductive scientists and couples attempting pregnancy are interested in identifying predictors of the day-specific probabilities of conception in relation to the timing of a single intercourse act. Because most menstrual cycles have multiple days of intercourse, the occurrence of conception represents the aggregation across Bernoulli trials for each intercourse day. Because of this data structure and dependency among the multiple cycles from a woman, implementing analyses has proven challenging. This article proposes a Bayesian approach based on a generalization of the Barrett and Marshall model to incorporate a woman-specific frailty and day-specific covariates. The model results in a simple closed form expression for the marginal probability of conception, and has an auxiliary variables formulation that facilitates efficient posterior computation. Although motivated by fecundability studies, the approach can be used for efficient variable selection and model averaging in general applications with categorical or discrete event time data.

Adult↗
PubMed2004

Genomic approaches in dissecting complex biological pathways.

Advances in genomic research have provided many types of large-scale data that contain rich information on various biological pathways. Intensive efforts have been made to qualitatively or quantitatively model biological pathways using these genomic data. Some general network properties, such as the scale-free property and network motifs, have been discussed and various network models have been applied to reconstruct pathways. However, there is a lack of systematic integration of prior knowledge and different genomic data in these analyses. In this review, we discuss pathway reconstruction under the consideration of the complexity embedded in the biological system, and the global and local properties of biological pathways. We review major methodologies, including clustering methods, scale-free networks models, Bayesian networks models, Boolean networks models, systems of differential equations, and data integration methods. We focus on the difficulty of each methodology in modeling biological pathways, and emphasize that different models capture different aspects of biological pathways or genomic data. The 'noisy' large-scale genomic data require the mathematical models and computational methods to be both robust and identifiable. In addition, we believe that ideal models should have the capability of incorporating various data types and these models need to be assessed through rigorous comparisons with empirical data.

Bayes Theorem↗
PubMed2004

Marginal modeling of multilevel binary data with time-varying covariates.

We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors, and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.

Adult↗
PubMed1994

Application of USC*PACK clinical programs to vancomycin in neutropenic patients.

The pharmacokinetics of vancomycin were studied in 10 neutropenic patients (4 male, 6 female) using the USC*PACK Clinical Programs. The experimental data was determined after the first administration of 1000 mg injected as a 1-h infusion. Eight blood samples were collected between 15 min and 11 h after the end of the infusion. Plasma vancomycin concentrations were measured by immunoassay procedure. Creatinine clearance and urine flow were also measured. Pharmacokinetic parameters were computed using a two-compartment model: Vc = 0.270665 +/- 0.161033 (l.kg-1); Kcp = 0.732927 +/- 0.464449 (h-1); Ks = 0.004952 +/- 0.00272 (min.ml-1.h-1); Kpc = 0.470243 +/- 0.194677 (h-1); Ki = 0.011675 +/- 0.004086 (h-1); Ke = 0.644415 +/- 0.239376 (h-1). When we compared this population to the general population of the program, Ke was increased. Elimination constant Ke was not correlated to either creatinine clearance or urine flow. Evaluation of the predictive performance of the Bayesian PC Program for adaptive control of vancomycin therapy in neutropenic patients is the next step of this study.

Adolescent↗
PubMed2004

Recursive bayesian decoding of motor cortical signals by particle filtering.

The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful when probability model assumptions are satisfied. We have implemented a recursive Bayesian algorithm for reconstructing hand movement from neurons in the motor cortex. The algorithm uses a recently developed numerical method known as "particle filtering" and follows the same general strategy as that used by Brown et al. to reconstruct the path of a foraging rat from hippocampal place cells. We investigated the method in a numerical simulation study in which neural firing rate was assumed to be positive, but otherwise a linear function of movement velocity, and preferred directions were not uniformly distributed. In terms of mean-squared error, the approach was approximately 10 times more efficient than the PV algorithm and 5 times more efficient than OLE. Thus use of recursive Bayesian decoding can achieve the accuracy of the PV algorithm (or OLE) with approximately 10 times (or 5 times) fewer neurons. The method was also used to reconstruct hand movement in an ellipse-drawing task from 258 cells in the ventral premotor cortex. Recursive Bayesian decoding was again more efficient than the PV and OLE methods, by factors of roughly seven and three, respectively.

Algorithms↗
PubMed2001

Bayesian 2-D deconvolution: effect of using spatially invariant ultrasound point spread functions.

Observed ultrasound images are degraded representations of the-true tissue reflectance. The specular reflections at boundaries between regions of different tissue types are blurred, and the diffuse scattering within homogenous regions causes speckle because of the oscillating nature of the transmitted pulse. To reduce both blur and speckle, we have developed algorithms for the restoration of simulated and real ultrasound images based on Markov random field models and Bayesian statistical methods. The algorithm is summarized here, although a more detailed description can be found in our companion paper [1]. Because the point spread function (psf) is unknown, we investigate the effects of using incorrect frequencies and sizes for the model psf during the restoration process. First, we degrade the images either with a known simulated psf or a measured psf. Then, we use different psf shapes during restoration to study the robustness of the method. We found that small variations in the parameters characterizing the psf, less than +/- 25% change in frequency, width, or length, still yielded satisfactory results. When altering the psf more than this, the restorations were not acceptable. The restorations were particularly sensitive to large increases in the restoring psf frequency. Thus, 2-D Bayesian restoration using a fixed psf may yield acceptable results as long as the true variant psfs have not varied too much during imaging.

Bayes Theorem↗
PubMed2001

Bayesian regression methodology for estimating a receiver operating characteristic curve with two radiologic applications: prostate biopsy and spiral CT of ureteral stones.

RATIONALE AND OBJECTIVES: The authors evaluated two Bayesian regression models for receiver operating characteristic (ROC) curve analysis of continuous diagnostic outcome data with covariates. MATERIALS AND METHODS: Full and partial Bayesian regression models were applied to data from two studies (n = 180 and 100, respectively): (a) The diagnostic value of prostate-specific antigen (PSA) levels (outcome variable) for predicting disease after radical prostatectomy (gold standard) was evaluated for three risk groups (covariates) based on Gleason scores. (b) Spiral computed tomography was performed on patients with proved obstructing ureteral stones. The predictive value of stone size (outcome) was evaluated along with two treatment options (gold standard), as well as stone location (in or not in the ureterovesical junction [UVJ]) and patient age (covariates). Summary ROC measures were reported, and various prior distributions of the regression coefficients were investigated. RESULTS: (a) In the PSA example, the ROC areas under the full model were 0.667, 0.769, and 0.703, respectively, for the low-, intermediate-, and high-risk groups. Under the partial model, the area beneath the ROC curve was 0.706. (b) The ROC areas for patients with ureteral stones in the UVJ decreased dramatically with age but otherwise were close to that under the partial model (ie, 0.774). The prior distribution had greater influence in the second example. CONCLUSION: The diagnostic tests were accurate in both examples. PSA levels were most accurate for staging prostate cancer among intermediate-risk patients. Stone size was predictive of treatment option for all patients other than those 40 years or older and with a stone in the UVJ.

Adult↗