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At least 289 records · Page 16Linked to original sources
PubMed2005

Speed can go up as well as down at low contrast: implications for models of motion perception.

It is well-known that reducing the contrast of a slow moving stimulus reduces its apparent speed. [Thompson, P. (1982). Perceived rate of movement depends on contrast. Vision Research, 22, 377-380.] report of this finding also suggested that at speeds above 8 cycles/s reducing contrast increased perceived speed. However in a later report, Stone and Thompson (1992), using a more rigorous, forced-choice procedure, failed to collect reliable data at these higher speeds. Here, we confirm that faster moving stimuli can appear to move faster than their true speed at low contrasts and we propose a physiologically plausible ratio model that unlike recent Bayesian models (e.g. Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598-604) can account well for the results.

Bayes Theorem↗
PubMed1999

The use of a Bayesian forecasting model in the management of warfarin therapy after total hip arthroplasty.

This study was performed to compare the computer-based and physician-based management of warfarin therapy after total hip arthroplasty (THA). The computer-assisted and control groups of patients were placed on warfarin postoperatively and followed for a 1-month period. A significant difference (P<.05) was found between the mean number of days needed to reach therapeutic anticoagulation in the control group (4.7+/-3.0 days) and the experimental group (2.8+/-1.4 days) and the proportion of patients in each group who were discharged with a subtherapeutic international normalized ratio (INR) (INR <1.5). The computer-based management of warfarin therapy was more efficient than unaided physician-based management and therefore may lead to improved, cost-effective patient care by reducing length of hospital stay and complications attributable to nontherapeutic anticoagulation in THA patients.

Aged↗
PubMedDate not supplied

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction.

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in beta-sheets, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/-wild/bsm.html.

Algorithms↗
PubMed2004

A bayesian hierarchical model for the analysis of Affymetrix arrays.

An area of active research in DNA microarray analysis focuses on identifying differentially expressed genes between normal and malignant tissues. The analysis is complicated by the presence of several unreliable expression readings. Here, we illustrate a methodology where the expression estimates are modeled as censored data and discriminating genes are selected using ANOVA-based criteria.

Analysis of Variance↗
PubMed1996

[Current developments in biostatistics].

Recent developments in biostatistics in three key areas: survival analysis of censured data, generalised linear models for correlated data, Bayesian modelling of complex data, are presented. Particular emphasis is placed upon illustrating common trends in these areas, in particular advances in computer intensive methods, the development of unifying modelling structures and the increasing variety of complex biomedical problems which can be realistically analysed.

Bayes Theorem↗
PubMed2003

Estimation of infection and recovery rates for highly polymorphic parasites when detectability is imperfect, using hidden Markov models.

A Bayesian hierarchical model is proposed for estimating parasitic infection dynamics for highly polymorphic parasites when detectability of the parasite using standard tests is imperfect. The parasite dynamics are modelled as a non-homogeneous hidden two-state Markov process, where the observed process is the detection or failure to detect a parasitic genotype. This is assumed to be conditionally independent given the hidden process, that is, the underlying true presence of the parasite, which evolves according to a first-order Markov chain. The model allows the transition probabilities of the hidden states as well as the detectability parameter of the test to depend on a number of covariates. Full Bayesian inference is implemented using Markov chain Monte Carlo simulation. The model is applied to a panel data set of malaria genotype data from a randomized controlled trial of bed nets in Tanzanian children aged 6-30 months, with the age of the host and bed net use as covariates. This analysis confirmed that the duration of infections with parasites belonging to the MSP-2 FC27 allelic family increased with age.

Algorithms↗
PubMed1999

A Bayesian semiparametric accelerated failure time model.

A Bayesian semiparametric approach is described for an accelerated failure time model. The error distribution is assigned a Pólya tree prior and the regression parameters a noninformative hierarchical prior. Two cases are considered: the first assumes error terms are exchangeable; the second assumes that error terms are partially exchangeable. A Markov chain Monte Carlo algorithm is described to obtain a predictive distribution for a future observation given both uncensored and censored data.

Algorithms↗
PubMed2003

A Bayesian 3-compartment model for 99mTc-MAG3 clearance.

UNLABELLED: Because recent reports have questioned the traditional 2-compartment model for calculating tracer clearance after a single intravenous injection, a 3-compartment model was evaluated in this study. METHODS: Bayesian statistics were used, which facilitated curve fitting by treating all subjects simultaneously. (99m)Tc-Mercaptoacetyltriglycine clearance data from 154 adults and 109 children were measured at several centers, typically 6-9 plasma samples spanning 5-90 min, and fitted by 2- and 3-compartment Bayesian models. RESULTS: Clearance estimates were found to be systematically lower for the 3-compartment model than for the 2-compartment model. A single-sample procedure based on the 3-compartment model was found to eliminate most of the known discrepancy between formulas based on single-injection and continuous-infusion reference methods. CONCLUSION: A 3-compartment model led to lower and probably more accurate clearance estimates than the conventional 2-compartment model. A new single-sample method is presented, based on the 3-compartment model as reference standard.

Adolescent↗
PubMed1990

Popperian everyday diagnostics--the growth of diagnostic knowledge in the particular case.

In an earlier paper the Bayesian model for everyday diagnostics of disease in the particular patient and the Bayesian decision model was criticized. Here a Popperian model is applied and its presumptions and consequences are investigated. Abandoning calculable probabilities as Popper suggests in science, and substituting them with degrees of corroboration, is realistic.

Decision Theory↗
PubMed2004

Bayesian computation in recurrent neural circuits.

A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this article, we show that a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary hidden Markov model. We illustrate the approach using an orientation discrimination task and a visual motion detection task. In the case of orientation discrimination, we show that the model network can infer the posterior distribution over orientations and correctly estimate stimulus orientation in the presence of significant noise. In the case of motion detection, we show that the resulting model network exhibits direction selectivity and correctly computes the posterior probabilities over motion direction and position. When used to solve the well-known random dots motion discrimination task, the model generates responses that mimic the activities of evidence-accumulating neurons in cortical areas LIP and FEF. The framework we introduce posits a new interpretation of cortical activities in terms of log posterior probabilities of stimuli occurring in the natural world.

Action Potentials↗
PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗
PubMed2003

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment.

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

Algorithms↗
PubMed1993

How vancomycin is used in Australasia--a survey.

BACKGROUND: Vancomycin serum concentrations have been monitored over the last 30 years in an attempt to avoid dose-dependent toxicity and enhance efficacy. Current literature recommendations for peak and trough concentrations are usually in the range of 20-40 mg/L and 5-10 mg/L, respectively. Literature recommendations regarding the time at which peak concentrations are measured are highly variable, ranging from immediately after the end of the infusion to three hours post-dose. AIMS: To identify how vancomycin dosing is being monitored and assess variability in the current practice. METHODS: A survey of microbiology departments and infectious disease physicians in major Australasian hospitals was undertaken. The variability in the current practice was assessed by fitting mean recommendations to a two compartment Bayesian model. RESULTS: Of the 83 (70%) who replied 71 (86%) monitored vancomycin concentrations. Fifty-four percent targeted peak concentrations within the range of 20-40 mg/L, and 73% targeted trough concentrations < or = 10 mg/L. The time of sampling of peak concentrations varied considerably ranging from immediately (12%) to 120 minutes (12%) post-infusion (median 30 minutes [40%]). The concentration-time curves resulting from three sets of mean recommendations ('peaks' drawn at: 0, 30 and 120 minutes aiming for a concentration of 35 mg/L with a trough concentration of 10 mg/L) were modelled using a two compartment Bayesian programme. The predicted true peak (maximum) concentrations ranged from 30 to 86 mg/L, despite aiming for identical target concentrations, indicating marked variation in the actual dosing practice. CONCLUSIONS: There is thus considerable variation in the practice of vancomycin therapeutic monitoring which has a major effect on dosing. The main contributing factor is the variable timing of sampling peak concentrations.

Australia↗
PubMed2006

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span.

BACKGROUND: The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data. Here, the potential of such modeling is demonstrated by examining the 5,225 free-text items in the Caenorhabditis Genetic Center (CGC) Bibliography using techniques from statistical information retrieval. Items in the CGC biomedical text corpus were modeled using the Latent Dirichlet Allocation (LDA) model. LDA is a hierarchical Bayesian model which represents a document as a random mixture over latent topics; each topic is characterized by a distribution over words. RESULTS: An LDA model estimated from CGC items had better predictive performance than two standard models (unigram and mixture of unigrams) trained using the same data. To illustrate the practical utility of LDA models of biomedical corpora, a trained CGC LDA model was used for a retrospective study of nematode genes known to be associated with life span modification. Corpus-, document-, and word-level LDA parameters were combined with terms from the Gene Ontology to enhance the explanatory value of the CGC LDA model, and to suggest additional candidates for age-related genes. A novel, pairwise document similarity measure based on the posterior distribution on the topic simplex was formulated and used to search the CGC database for "homologs" of a "query" document discussing the life span-modifying clk-2 gene. Inspection of these document homologs enabled and facilitated the production of hypotheses about the function and role of clk-2. CONCLUSION: Like other graphical models for genetic, genomic and other types of biological data, LDA provides a method for extracting unanticipated insights and generating predictions amenable to subsequent experimental validation.

Animals↗
PubMed2005

Investigating the functional role of callosal connections with dynamic causal models.

The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human postmortem experiments are currently complemented by diffusion-weighted imaging, which enables noninvasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which interhemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a context-dependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms that manipulate interhemispheric interactions. In this article, we demonstrate that dynamic causal modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject.

Algorithms↗
PubMed2004

Safety related drug-labelling changes: findings from two data mining algorithms.

INTRODUCTION: With increasing volumes of postmarketing safety surveillance data, data mining algorithms (DMAs) have been developed to search large spontaneous reporting system (SRS) databases for disproportional statistical dependencies between drugs and events. A crucial question is the proper deployment of such techniques within the universe of methods historically used for signal detection. One question of interest is comparative performance of algorithms based on simple forms of disproportionality analysis versus those incorporating Bayesian modelling. A potential benefit of Bayesian methods is a reduced volume of signals, including false-positive signals. OBJECTIVE: To compare performance of two well described DMAs (proportional reporting ratios [PRRs] and an empirical Bayesian algorithm known as multi-item gamma Poisson shrinker [MGPS]) using commonly recommended thresholds on a diverse data set of adverse events that triggered drug labelling changes. METHODS: PRRs and MGPS were retrospectively applied to a diverse sample of drug-event combinations (DECs) identified on a government Internet site for a 7-month period. Metrics for this comparative analysis included the number and proportion of these DECs that generated signals of disproportionate reporting with PRRs, MGPS, both or neither method, differential timing of signal generation between the two methods, and clinical nature of events that generated signals with only one, both or neither method. RESULTS: There were 136 relevant DECs that triggered safety-related labelling changes for 39 drugs during a 7-month period. PRRs generated a signal of disproportionate reporting with almost twice as many DECs as MGPS (77 vs 40). No DECs were flagged by MGPS only. PRRs highlighted DECs in advance of MGPS (1-15 years) and a label change (1-30 years). For 59 DECs, there was no signal with either DMA. DECs generating signals of disproportionate reporting with only PRRs were both medically serious and non-serious. DISCUSSION/CONCLUSION: In most instances in which a DEC generated a signal of disproportionate reporting with both DMAs (almost twice as many with PRRs), the signal was generated using PRRs in advance of MGPS. No medically important events were signalled only by MGPS. It is likely that the incremental utility of DMAs are highly situation-dependent. It is clear, however, that the volume of signals generated by itself is an inadequate criterion for comparison and that clinical nature of signalled events and differential timing of signals needs to be considered. Accepting commonly recommended threshold criteria for DMAs examined in this study as universal benchmarks for signal detection is not justified.

Algorithms↗
PubMed2005

Reduced sampling schedule for the glucose minimal model: importance of Bayesian estimation.

The minimal model (MM) of glucose kinetics during an intravenous glucose tolerance test (IVGTT) is widely used in clinical studies to measure metabolic indexes such as glucose effectiveness (S(G)) and insulin sensitivity (S(I)). The standard (frequent) IVGTT sampling schedule (FSS) for MM identification consists of 30 points over 4 h. To facilitate clinical application of the MM, reduced sampling schedules (RSS) of 13-14 samples have also been derived for normal subjects. These RSS are especially appealing in large-scale studies. However, with RSS, the precision of S(G) and S(I) estimates deteriorates and, in certain cases, becomes unacceptably poor. To overcome this difficulty, population approaches such as the iterative two-stage (ITS) approach have been recently proposed, but, besides leaving some theoretical issues open, they appear to be oversized for the problem at hand. Here, we show that a Bayesian methodology operating at the single individual level allows an accurate determination of MM parameter estimates together with a credible measure of their precision. Results of 16 subjects show that, in passing from FSS to RSS, there are no significant changes of point estimates in nearly all of the subjects and that only a limited deterioration of parameter precision occurs. In addition, in contrast with the previously proposed ITS method, credible confidence intervals (e.g., excluding negative values) are obtained. They can be crucial for a subsequent use of the estimated MM parameters, such as in classification, clustering, regression, or risk analysis.

Adult↗