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At least 19 recordsLinked to original sources
PubMed2005

Case Study Part 2: Probabilistic modelling of long-term effects of pesticides on individual breeding success in birds and mammals.

Long term exposure of skylarks to a fictitious insecticide and of wood mice to a fictitious fungicide were modelled probabilistically in a Monte Carlo simulation. Within the same simulation the consequences of exposure to pesticides on reproductive success were modelled using the toxicity-exposure-linking rules developed by R.S. Bennet et al. (2005) and the interspecies extrapolation factors suggested by R. Luttik et al. (2005). We built models to reflect a range of scenarios and as a result were able to show how exposure to pesticide might alter the number of individuals engaged in any given phase of the breeding cycle at any given time and predict the numbers of new adults at the season's end.

Animals↗
PubMed2005

Evolutionary models for insertions and deletions in a probabilistic modeling framework.

BACKGROUND: Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time. Probabilistic models of substitution events are well established, but there has not been a completely satisfactory theoretical framework for modeling insertion and deletion events. RESULTS: I have developed a method for extending standard Markov substitution models to include gap characters, and another method for the evolution of state transition probabilities in a probabilistic model. These methods use instantaneous rate matrices in a way that is more general than those used for substitution processes, and are sufficient to provide time-dependent models for standard linear and affine gap penalties, respectively. Given a probabilistic model, we can make all of its emission probabilities (including gap characters) and all its transition probabilities conditional on a chosen divergence time. To do this, we only need to know the parameters of the model at one particular divergence time instance, as well as the parameters of the model at the two extremes of zero and infinite divergence. I have implemented these methods in a new generation of the RNA genefinder QRNA (eQRNA). CONCLUSION: These methods can be applied to incorporate evolutionary models of insertions and deletions into any hidden Markov model or stochastic context-free grammar, in a pair or profile form, for sequence modeling.

Algorithms↗
PubMed2003

Probabilistic modelling of human exposure to intense sweeteners in Italian teenagers: validation and sensitivity analysis of a probabilistic model including indicators of market share and brand loyalty.

For the assessment of exposure to food-borne chemicals, the most commonly used methods in the European Union follow a deterministic approach based on conservative assumptions. Over the past few years, to get a more realistic view of exposure to food chemicals, risk managers are getting more interested in the probabilistic approach. Within the EU-funded 'Monte Carlo' project, a stochastic model of exposure to chemical substances from the diet and a computer software program were developed. The aim of this paper was to validate the model with respect to the intake of saccharin from table-top sweeteners and cyclamate from soft drinks by Italian teenagers with the use of the software and to evaluate the impact of the inclusion/exclusion of indicators on market share and brand loyalty through a sensitivity analysis. Data on food consumption and the concentration of sweeteners were collected. A food frequency questionnaire aimed at identifying females who were high consumers of sugar-free soft drinks and/or of table top sweeteners was filled in by 3982 teenagers living in the District of Rome. Moreover, 362 subjects participated in a detailed food survey by recording, at brand level, all foods and beverages ingested over 12 days. Producers were asked to provide the intense sweeteners' concentration of sugar-free products. Results showed that consumer behaviour with respect to brands has an impact on exposure assessments. Only probabilistic models that took into account indicators of market share and brand loyalty met the validation criteria.

Adolescent↗
PubMed2005

Does god play dice with schizophrenia? A probabilistic model for the understanding of causation in mental illness.

While the pure sciences have moved over the XX century from the certainties of the Laplacian mechanistic models to probabilistic ones, such as quantum physics, the attempts to explain the causation in mental illness are still stuck to deterministic frameworks which are deceptively reassuring, but also too simplistic to approach problems of such a complexity. The deterministic approach has led to several false leads that have affected research in the aetiology in mental illness, especially schizophrenia, and only moving to probabilistic models will allow an understanding of the causal processes. In this paper is challenged the almost universally accepted assumption that, to explain the lack of complete concordance amongst identical twins in schizophrenia, there is the need to resort to environmental aetiological agents. It is described instead how, using a probabilistic framework, a polygenic model without threshold can satisfactorily explain the data available so far without having to resort to environmental factors. The genes predisposing to schizophrenia, probably through a derailment of the neurodevelopment, would have a normal distribution in the general population, while the liability for the illness will increase exponentially with the number of risk genes, with a small minority of the population being at high risk. The average genetic liability to develop schizophrenia in individuals who later are affected by the disorder is around 50%. The adoption of a probabilistic model for the understanding of the inheritance of schizophrenia has therefore far reaching consequences, revolutionizing the interpretation of the twin studies, allowing a consistent model which does not require environmental interventions in the aetiology of schizophrenia and offering a framework to make sense of findings from the epidemiological research which are not satisfactorily explained by the deterministic models adopted so far.

Genetic Predisposition to Disease↗
PubMed2000

Probabilistic modelling: theory and practice.

Probabilistic modelling techniques allow much more realistic estimates of exposure and risk by computing the use of the full range of potential exposures rather than single 'worst case' exposures. However, these techniques require additional considerations regarding the appropriate data and models. This article reviews the theoretical aspects of probabilistic modelling and also considers some of the practical applications. The most common method, called Monte Carlo analysis, is discussed in some detail. The practical application of Monte Carlo to risk assessments is presented along with an evaluation of the input parameters. Topics also discussed include considerations of the requirements for precision and procedures for validation of assessments.

Food Contamination↗
PubMed2026

TreeFlow: Probabilistic Modelling and Automatic Differentiation for Phylogenetics.

Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic modelling and automatic differentiation with phylogenetic trees. It embeds phylogenetic trees in the TensorFlow Probability framework, and implements inference algorithms for phylogenetic models given a fixed tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

Bayesian inference↗
PubMed2006

Probabilistic models of language processing and acquisition.

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.

Brain↗
PubMed2006

A probabilistic model for the MRMC method, part 2: validation and applications.

RATIONALE AND OBJECTIVES: We have previously described a probabilistic model for the multiple-reader, multiple-case paradigm for receiver operating characteristic analysis. When the figure of merit is the Wilcoxon statistic, this model returns a seven-term expansion for the variance of this statistic as a function of the numbers of cases and readers. This probabilistic model also provides expressions for the coefficients in the seven-term expansion in terms of expectations over the internal noise, readers, and cases. Finally, this probabilistic model sets bounds on both the overall variance of the Wilcoxon statistic and the individual coefficients. MATERIALS AND METHODS: In this article, we will first validate the probabilistic model by comparing variances determined by direct computation of the expansion coefficients to empirical estimates of the variance using independent sampling. Validation of the probabilistic model will enable us to use the direct estimates of the expansion coefficients as a gold standard to compare other coefficient-estimation techniques. Next, we develop a coefficient-estimation technique that employs bootstrapping to estimate the Wilcoxon statistic variance for different numbers of readers and cases. We then employ constrained, least-squares fitting techniques to estimate the expansion coefficients. The constraints used in this fitting are derived directly from the probabilistic model. RESULTS: Using two different simulation studies, we show that the novel (and practical) bootstrapping/fitting technique returns estimates of the coefficients that are consistent with the gold standard. CONCLUSION: The results presented also serve to validate the seven-term expansion for the variance of the Wilcoxon statistic.

Analysis of Variance↗
PubMed2003

Validation and sensitivity analysis of probabilistic models of dietary exposure to micronutrients: an example based on vitamin B6.

Probabilistic modelling can be used to get an insight into the variability and uncertainty of the nutrient intake in a population. When a probabilistic model is used, it is important that it is validated. Furthermore, a sensitivity analysis of the model output can give an insight into the most important input variables of the model and can be used as an aid to describe the reliability of the model. In this study, four models to estimate vitamin B(6) intake among males and females were validated using the method of Kaaks et al. This method compares the relationship between three different kind of measurements with the unknown 'true' intake. In each of these four models, only one input variable (concentration or bioavailability) was changed compared with a reference model. A sensitivity analysis was also performed. The results of the validation showed that for males, a model using a fixed bioavailability factor at the food group level was valid, while for females a model using either a fixed value or a distribution for the bioavailability factor was valid. Use of a distribution for the concentration of vitamin B(6) in supplements was not valid. The results of the sensitivity analysis showed that the concentration of vitamin B(6) in food and supplements was the key contributor to variability and uncertainty in the model estimates of vitamin B(6) intake, in both males and females. All results indicated that when taking variability and uncertainty into account by using probabilistic modelling, the effect on the nutrient intake for nutrients that are present in many common eaten foods, is small. For these broadly available nutrients, fixed concentrations and bioavailability factors give a good estimate of the nutrient intake in a population. When using probabilistic modelling, it is very important to collect more actual information about the concentration.

Adult↗
PubMed2003

A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface.

A hybrid probabilistic modeling approach that integrates artificial neural networks (ANNs) with statistical Bayesian conditional probability estimation is proposed. The suggested approach benefits from the power of ANNs as highly flexible nonlinear mapping paradigms, and the Bayes' theorem for computing probabilities of bacterial growth with the aid of Parzen's probability distribution function estimators derived for growth and no-growth (G/NG) states. The proposed modeling approach produces models that can predict the probability of growth of targeted microorganism as affected by a set of parameters pertaining to extrinsic factors and operating conditions. The models also can be used to define the probabilistic boundary (interface) between growth and no-growth, and as such can define and predict the values of critical parameters required to keep a desired pre-specified bacterial growth risk in check. A modular system incorporating the various computational modules was constructed to illustrate the application of the hybrid approach to the probabilistic modeling of growth of pathogenic Escherichia coli strain as affected by temperature and water activity. The proposed approach was compared to other techniques including the traditional linear and nonlinear logistic regression. Results indicated that the hybrid approach outperforms the other approaches in its accuracy as well as flexibility to extract the implicit interrelationships between the various parameters. Advantages and limitations of the approach were also discussed and compared to those of other techniques.

Bayes Theorem↗
PubMed2006

Assessment of uncertainty in a probabilistic model of consumer exposure to pesticide residues in food.

The assessment of consumer exposure to pesticides is an important part of pesticide regulation. Probabilistic modelling allows analysis of uncertainty and variability in risk assessments. The output of any assessment will be influenced by the characteristics and uncertainty of the inputs, model structure and assumptions. While the use of probabilistic models is well established in the United States, in Europe problems of low acceptance, sparse data and lack of guidelines are slowing the development. The analyses in the current paper focused on the dietary pathway and the exposure of UK toddlers. Three single food, single pesticide case studies were used to parameterize a simple probabilistic model built in Crystal Ball. Data on dietary consumption patterns were extracted from National Diet and Nutrition Surveys, and levels of pesticide active ingredients in foods were collected from Pesticide Residues Committee monitoring. The effect of uncertainty on the exposure estimate was analysed using scenarios, reflecting different assumptions related to sources of uncertainty. The most influential uncertainty issue was the distribution type used to represent input variables. Other sources that most affected model output were non-detects, unit-to-unit variability and processing. Specifying correlation between variables was found to have little effect on exposure estimates. The findings have important implications for how probabilistic modelling should be conducted, communicated and used by policy and decision makers as part of consumer risk assessment of pesticides.

Body Weight↗
PubMed1985

Global asymptotic stability of the size distribution in probabilistic models of the cell cycle.

Probabilistic models of the cell cycle maintain that cell generation time is a random variable given by some distribution function, and that the probability of cell division per unit time is a function only of cell age (and not, for instance, of cell size). Given the probability density, f(t), for time spent in the random compartment of the cell cycle, we derive a recursion relation for psi n(x), the probability density for cell size at birth in a sample of cells in generation n. For the case of exponential growth of cells, the recursion relation has no steady-state solution. For the case of linear cell growth, we show that there exists a unique, globally asymptotically stable, steady-state birth size distribution, psi*(x). For the special case of the transition probability model, we display psi*(x) explicitly.

Cell Cycle↗
PubMed2006

Use of probabilistic modeling within a physiologically based pharmacokinetic model to predict sulfamethazine residue withdrawal times in edible tissues in swine.

The presence of antimicrobial agents in edible tissues of food-producing animals remains a major public health concern. Probabilistic modeling techniques incorporated into a physiologically based pharmacokinetic (PBPK) model were used to predict the amounts of sulfamethazine residues in edible tissues in swine. A PBPK model for sulfamethazine in swine was adapted to include an oral dosing route. The distributions for sensitive parameters were determined and were used in a Monte Carlo analysis to predict tissue residue times. Validation of the distributions was done by comparison of the results of a Monte Carlo analysis to those obtained with an external data set from the literature and an in vivo pilot study. The model was used to predict the upper limit of the 95% confidence interval of the 99th percentile of the population, as recommended by the U.S. Food and Drug Administration (FDA). The external data set was used to calculate the withdrawal time by using the tolerance limit algorithm designed by FDA. The withdrawal times obtained by both methods were compared to the labeled withdrawal time for the same dose. The Monte Carlo method predicted a withdrawal time of 21 days, based on the amounts of residues in the kidneys. The tolerance limit method applied to the time-limited data set predicted a withdrawal time of 12 days. The existing FDA label withdrawal time is 15 days. PBPK models can incorporate probabilistic modeling techniques that make them useful for prediction of tissue residue times. These models can be used to calculate the parameters required by FDA and explore those conditions where the established withdrawal time may not be sufficient.

Animals↗
PubMed2001

A physically based, probabilistic model for ultrasonic images incorporating shape, microstructure, and system characteristics.

Recent successes with Bayesian methods for analysis of shape in medical images have motivated our development of a shape-based data likelihood for ultrasound, the foundation of which is a computationally feasible, pixel-based, probabilistic image model. Previous probabilistic models for ultrasound generally assume an analytic form, e.g., Rayleigh, Rician, K, Generalized K, etc., then attempt to fit data to the model. Assumptions and intensive computation inherent in such an approach make it unsuitable for our purposes. In the pursuit of a new model, we have described previously a physical model for image formation that incorporates the imaging system characteristics, the surface shape, and the surface microstructure. That physical model was validated via a visual comparison of simulated and actual images of a cadaveric vertebra. In this work, a random phasor sum representation of the physical model provides the basis for a probabilistic form. In contrast to existing probabilistic models, we compute the amplitude mean and variance directly from the physical model. These statistics can be displayed as images to characterize the tissue, but, more importantly, they permit the subsequent assignment of a suitable density function to each pixel for the purposes of constructing a data likelihood. The order of these steps, i.e., first computing the statistics and then assigning a density function, permits the inclusion of the local surface shape, the surface microstructure, and the system characteristics at every image pixel without violating the physical model. Currently, the value of the SNR0, the ratio of the mean to the standard deviation, is used to estimate whether a pixel is Rayleigh- or non-Rayleigh-distributed. This assessment forms the basis for a data likelihood constructed as a product of Rayleigh and Gaussian density functions describing the individual image pixels.

Humans↗
PubMed2002

A new discriminative kernel from probabilistic models.

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Models, Neurological↗
PubMed2004

Application of a new probabilistic model for recognizing complex patterns in glycans.

MOTIVATION: The study of carbohydrate sugar chains, or glycans, has been one of slow progress mainly due to the difficulty in establishing standard methods for analyzing their structures and biosynthesis. Glycans are generally tree structures that are more complex than linear DNA or protein sequences, and evidence shows that patterns in glycans may be present that spread across siblings and into further regions that are not limited by the edges in the actual tree structure itself. Current models were not able to capture such patterns. RESULTS: We have applied a new probabilistic model, called probabilistic sibling-dependent tree Markov model (PSTMM), which is able to inherently capture such complex patterns of glycans. Not only is the ability to capture such patterns important in itself, but this also implies that PSTMM is capable of performing multiple tree structure alignments efficiently. We prove through experimentation on actual glycan data that this new model is extremely useful for gaining insight into the hidden, complex patterns of glycans, which are so crucial for the development and functioning of higher level organisms. Furthermore, we also show that this model can be additionally utilized as an innovative approach to multiple tree alignment, which has not been applied to glycan chains before. This extension on the usage of PSTMM may be a major step forward for not only the structural analysis of glycans, but it may consequently prove useful for discovering clues into their function.

Algorithms↗
PubMed2005

A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips.

MOTIVATION: Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarization methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large datasets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and does not account for specific binding to the mismatch (MM) probes. RESULTS: We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark datasets and a real time-course dataset, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions. AVAILABILITY: Both mgMOS and the new model multi-mgMOS have been implemented in an R package, which is available at http://www.bioinf.man.ac.uk/resources/puma.

Algorithms↗
PubMed2006

Refining a probabilistic model for interpreting verbal autopsy data.

OBJECTIVE: To build on the previously reported development of a Bayesian probabilistic model for interpreting verbal autopsy (VA) data, attempting to improve the model's performance in determining cause of death and to reassess it. DESIGN: An expert group of clinicians, coming from a wide range geographically and in terms of specialization, was convened. Over a four-day period the content of the previous probabilistic model was reviewed in detail and adjusted as necessary to reflect the group consensus. The revised model was tested with the same 189 VA cases from Vietnam, assessed by two local clinicians, that were used to test the preliminary model. RESULTS: The revised model contained a total of 104 indicators that could be derived from VA data and 34 possible causes of death. When applied to the 189 Vietnamese cases, 142 (75.1%) achieved concordance between the model's output and the previous clinical consensus. The remaining 47 cases (24.9%) were presented to a further independent clinician for reassessment. As a result, consensus between clinical reassessment and the model's output was achieved in 28 cases (14.8%); clinical reassessment and the original clinical opinion agreed in 8 cases (4.2%), and in the remaining 11 cases (5.8%) clinical reassessment, the model, and the original clinical opinion all differed. Thus overall the model was considered to have performed well in 170 cases (89.9%). CONCLUSIONS: This approach to interpreting VA data continues to show promise. The next steps will be to evaluate it against other sources of VA data. The expert group approach to determining the required probability base seems to have been a productive one in improving the performance of the model.

Autopsy↗