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At least 253 records · Page 14Linked to original sources
PubMed1998

Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model.

This paper tests the performance of a simple empirical scoring function on a set of candidate designs produced by a de novo design package. The scoring function calculates approximate ligand-receptor binding affinities given a putative binding geometry. To our knowledge this is the first substantial test of an empirical scoring function of this type on a set of molecular designs which were then subsequently synthesised and assayed. The performance illustrates that the methods used to construct the scoring function and the reliance on plausible, yet potentially false, binding modes can lead to significant over-prediction of binding affinity in bad cases. This is anticipated on theoretical grounds and provides caveats on the reliance which can be placed when using the scoring function as a screen in the choice of molecular designs. To improve the predictability of the scoring function and to understand experimental results, it is important to perform subsequent Quantitative Structure-Activity Relationship (QSAR) studies. In this paper, Bayesian regression is performed to improve the predictability of the scoring function in the light of the assay results. Bayesian regression provides a rigorous mathematical framework for the incorporation of prior information, in this case information from the original training set, into a regression on the assay results of the candidate molecular designs. The results indicate that Bayesian regression is a useful and practical technique when relevant prior knowledge is available and that the constraints embodied in the prior information can be used to improve the robustness and accuracy of regression models. We believe this to be the first application of Bayesian regression to QSAR analysis in chemistry.

Bayes Theorem↗
PubMed1997

Factors affecting susceptibility to intramammary infection and mastitis: an approximate Bayesian analysis.

Susceptibility to IMI and to mastitis in Holstein cows was studied using logistic mixed effects models and an approximate Bayesian analysis. Dichotomous response variables were the presence or absence of IMI, caused by any microorganism. IMI caused by Staphylococcus spp. or Corynebacterium spp., and clinical mastitis caused by any microorganism at specific lactation stages. Data included 619 lactation records from 282 cows. Fixed explanatory variables in the model were period, season and age at calving, lactation number, log-transformed SCC, and a joint effect of age and log SCC. Because random cow effects were assumed to be normally distributed and to have an unknown variance, this parameter was estimated by approximate marginal maximum likelihood. Results from the Bayesian analysis were contrasted with maximum likelihood estimates obtained from a fixed effects logistic model that ignored cow effects. Posterior mode and maximum likelihood estimates of location parameters were similar, although standard errors of the maximum likelihood estimates understated uncertainty. The IMI status during a previous lactation was a poor predictor of IMI status in subsequent lactations, and susceptibility increased as SCC increased. Interlactation (logit scale) repeatability estimates of susceptibility ranged from 0.22 to 0.23. A Taylor series expansion was used to approximate correlations between lactations on a binary scale. These correlations depended on associated fixed effects and ranged between 0.12 and 0.18, which were lower than correlations using the logit scale.

Animals↗
PubMed1996

Bayesian analysis of foraging by pigeons (Columba livia).

In this article, the authors combine models of timing and Bayesian revision of information concerning patch quality to predict foraging behavior. Pigeons earned food by pecking on 2 keys (patches) in an experimental chamber. Food was primed for only 1 of the patches on each trial. There was a constant probability of finding food in a primed patch, but it accumulated only while the animals searched there. The optimal strategy was to choose the better patch first and remain for a fixed duration, thereafter alternating evenly between the patches. Pigeons were nonoptimal in 3 ways: (a) they departed too early, (b) their departure times were variable, and (c) they were biased in their choices after initial departure. The authors review various explanations of these data.

Animals↗
PubMed2025

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem↗
PubMed1999

Model-independent mean-field theory as a local method for approximate propagation of information.

We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is 'model-independent' in the sense that we do not assume a particular type of dependences; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependences. In general, there are multiple solutions to the mean-field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean-field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared with the exact results.

Child↗
PubMed2025

ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.

Phylodynamic models capture joint epidemiological-evolutionary dynamics during an outbreak, providing a powerful tool to enhance understanding and management of disease transmission. Existing phylodynamic approaches, however, mostly rely on various non-mechanistic or semi-mechanistic approximations of the underlying epidemiological-evolutionary process. Previous work by Lau and colleagues has shown that full Bayesian mechanistic models, without relying on these approximations, can enable highly accurate joint inference of the epidemiological-evolutionary dynamics including the unobserved transmission tree. However, the Lau method faces major computational bottlenecks. As the volume of genomic data collected during outbreaks continues to grow, it is crucial to develop scalable yet accurate phylodynamic methods. Here we propose a new Bayesian phylodynamic model, overcoming the major scalability issue in the previous method and enabling a readily deployable, yet accurate, phylodynamic modeling framework. Specifically, we develop a scalable spatio-temporal phylodynamic framework for inferring the transmission tree (ScITree) and other key epidemiological parameters considering the infinite sites assumption in modeling mutation on the sequence level, in contrast to the Lau method in which mutation was modeled explicitly on the nucleotide level. Our approach features full Bayesian implementation utilizing an exact likelihood to mechanistically integrate epidemiological and evolutionary processes. We develop a computationally-efficient data-augmentation Markov Chain Monte Carlo algorithm, inferring key model parameters and unobserved dynamics including the transmission tree. We assess performance of our method using multiple simulated outbreak datasets. Our results indicate that our method can achieve high inference accuracy, comparable to the performance of the Lau method. Additionally, our method scales significantly more efficiently for large outbreaks, with computing time increasing linearly with outbreak size, compared to the exponential scaling of the Lau method. We also demonstrate our method's utility by applying our validated modeling framework to a dataset describing a foot-and-mouth disease outbreak in the UK. Our results show that our method is able to generate estimates of the transmission dynamics consistent with those from the prior method, further demonstrating the robustness of our new approach. In summary, our method provides a computationally-efficient, highly scalable, accurate modeling framework for inferring the joint spatio-temporal dynamics of epidemiological and evolutionary processes, facilitating timely and effective outbreak responses in space and time. Our method is implemented in our R package ScITree.

Bayes Theorem↗
PubMed1994

Adaptive control of drug dosage regimens: basic foundations, relevant issues, and clinical examples.

In this paper we examine several of the fundamental foundations and relevant clinical issues in adaptive control of drug dosage regimens for patients. Truly individualized therapy with drugs having narrow margins of safety first requires a practical pharmacokinetic/dynamic model of the behavior of a drug. Past experience with a drug is stored in the form of a population model. Next, using the information in such a model and its relationship to the incidence of adverse reactions, a specific, explicit therapeutic goal must be selected by the responsible clinician, based on the patient's need for the drug and the risk of adverse reactions felt to be justified by each patient's need, small, moderate, or great. Individualized drug therapy thus begins with the selection of individualized therapeutic goals (low, moderate, or high) for each patient. Using subsequent feedback from the patient's serum drug levels, and using Bayesian fitting, the model is then linked to each patient as a patient-specific model. Control of the model by the dosage regimen increasingly controls the patient, to better obtain the desired explicit therapeutic goals. This process is essentially similar to that of a flight control or missile guidance system.

Bayes Theorem↗
PubMed1997

Reasoning requirements for diagnosis of heart disease.

Over the past dozen years, the Heart Disease Program (HDP) has been developed to assist physicians in reasoning about cardiovascular disorders. Driven by several evaluations, the inference mechanism has progressed from a logic based model, to a Bayesian Probability Network (BPN) and finally a pseudo-Bayesian network with temporal and severity reasoning. Though aspects of cardiovascular reasoning are handled well by BPNs, temporal reasoning, homeostatic feedback mechanisms and effects of disease severities require additional inference strategies. This article discusses how these reasoning problems are handled, and deals with closely linked issues in building the user interface to collect detailed cardiovascular data and provide clear explanations of diagnoses.

Artificial Intelligence↗
PubMed1997

Automated analysis of the American College of Radiology mammographic accreditation phantom images.

A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology (ACR) mammographic accreditation phantom (MAP) images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles (five of which were specially trained in mammography) viewed the original film images under similar conditions and provided individual scores for each test object (fibrils, microcalcifications, and nodules). Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast threshold established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils (agreement occurred for the other test objects). The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed.

Accreditation↗
PubMed1997

Colorectal cancer mass-screening: estimation of faecal occult blood test sensitivity, taking into account cancer mean sojourn time.

Mass screening using the faecal occult blood test (FOBT) can reduce mortality from colorectal cancer. Reliable estimation of FOBT sensitivity is crucial in assessing the potential effectiveness of a mass-screening procedure. Available estimates could be inaccurate because they neglect the temporal aspect of screening. The aim of our study was to estimate the sensitivity of the FOBT in mass screening for colorectal cancer, taking into account the duration of the pre-clinical phase of the disease assessed by the mean sojourn time (MST), and to assess whether MST and FOBT sensitivity differ according to cancer subsite. We analysed data taken from the first round of the mass-screening programme of the department of Calvados (France), involving 164,364 subjects of whom 43.4% participated in FOB screening. MST and sensitivity were estimated using a simple empirical approach, a traditional maximum likelihood method and log-linear modelling using the Bayesian technique of Gibbs sampling. MST was estimated as between 4.5 and 5 years for all subsites combined. According to the Gibbs sampling method, MSTs were 3.5, 6.4 and 2.6 years for proximal colon, distal colon and rectal cancer, respectively. Our estimation methods give a low sensitivity for the FOBT (50%), results for different subsites being closer to each other, slightly higher for proximal cancer. Our results strongly suggest that tumour growth rates are very different according to subsite, slowest for distal cancer and speediest for rectal cancer. Consideration of FOBT sensitivity without MST appears unreliable. Our results by subsite suggest that combining FOBT and sigmoidoscopy could be a good strategy for colorectal cancer screening.

Aged↗
PubMed1997

Meta-analysis for the evaluation of potential surrogate markers.

We describe a meta-analysis approach for the evaluation of a potential surrogate marker. Surrogate markers are useful in helping to identify therapeutic mechanisms of action and disease pathogenesis, and for selecting therapies to take forward from phase II to phase III clinical trials. They have also become increasingly important for regulatory purposes by providing a basis for preliminary approval of drugs pending clinical outcome studies. Methodology for evaluating surrogate markers has focused on determining the difference in the effects of two treatments on clinical outcome in an individual clinical trial, and then estimating the proportion of this difference explained by the treatment's effects on the potential marker. Studies are, however, frequently underpowered or cease before they accumulate sufficient evidence to draw strong conclusions about the value of a potential surrogate marker using this approach, and there are also some technical difficulties with the approach. Consideration of the association between the difference in treatment effects on the clinical outcome and the difference in treatment effects on the potential marker over a range of trials provides an alternative means to evaluate a potential marker. We describe a meta-analysis approach using Bayesian methods to model this association. Importantly, this approach enables one to obtain prediction intervals for the true difference in clinical outcome for a given estimated treatment difference in the effect on the potential marker. We illustrate the methodology by applying it to results from studies of the AIDS Clinical Trials Group to assess the value of CD4 T-lymphocyte cell count as a potential surrogate marker for the treatment effects on the development of AIDS or death.

Anti-HIV Agents↗
PubMed1998

Approximate Bayesian inference for random effects meta-analysis.

Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formulae that provide an efficient tool for a qualitative analysis and a quick numerical estimation of posterior quantities. They are shown to lead to sensible approximations in two examples of meta-analyses and to be in broad agreement with the more computationally intensive Gibbs sampling.

Antibiotic Prophylaxis↗
PubMedDate not supplied

A comparison of various estimators of a treatment difference for a multi-centre clinical trial.

When a clinical trial is conducted at more than one centre it is likely that the true treatment effect will not be identical at each centre. In other words there will be some degree of treatment-by-centre interaction. A number of alternative approaches for dealing with this have been suggested in the literature. These include frequentist approaches with a fixed or random effects model for the observed data and Bayesian approaches. In the fixed effects model, there are two common competing estimators of the treatment difference, based on weighted or unweighted estimates from individual centres. Which one of these should be used is the subject of some controversy and we do not intend to take a particular methodological position in this paper. Our intention is to provide some insight into the relative merits of the indicated range of possible estimators of the treatment effect. For the fixed effects model, we also look at the merits of using a preliminary test for interaction assuming a 10 per cent significance level for the test. In order to make comparisons we have simulated a 'typical' trial which compares an active drug with a placebo in the treatment of hypertension, using systolic blood pressure as the primary variable. As well as allowing the treatment effect to vary between centres, we have concentrated on the particular case where one centre is out of line with the others in terms of its true treatment difference. The various estimators that result from the different approaches are compared in terms of mean squared error and power to reject the null hypothesis of no treatment difference. Overall, the approach that uses the fixed effects weighted estimator of overall treatment difference is recommended as one that has much to offer.

Analysis of Variance↗
PubMed1986

"Mental imbalance" and the prediction of recurrent delinquent behavior.

Cognitive and personality patterns of 84 court-referred adolescents were examined to identify predictors of recurrent delinquent behavior. Continued behavioral problems at follow-up were more likely in adolescents with discrepancies between Verbal and Performance IQ or large differences between "neurotic" and "psychotic" scale elevations on the MMPI. Positive outcomes were most likely for adolescents who could be described as "mildly neurotic." Combining the discrepancy scores from the intelligence and personality tests with other background variables in a Bayesian conditional probability model resulted in accurate predictions of later behavior for 81% of the sample. These findings suggest that imbalances in cognitive and personality development may limit a delinquent adolescent's ability to interact appropriately with the environment.

Adaptation, Psychological↗
PubMed1989

31P NMR Bayesian spectral analysis of rat brain in vivo.

Bayesian spectrum analysis for parameter estimation is a rigorous statistical (non-Fourier-based) method. Herein the Bayesian quadrature NMR model is introduced and applied to analysis of 31P NMR time domain data from in vivo rat brain. Immunity to both the brain spectrum "baseline hump" and the phase twist is demonstrated.

Animals↗
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↗