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Results for “probabilistic modelling”

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PubMed2000

Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration.

Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6.0, 5.0, 4.0, and 3.0), and potassium sorbate (0, 50, 100, 200, 500, and 1,000 ppm). The proposed model predicts the probability of growth under a set of conditions and calculates critical values of a(w), pH, and potassium sorbate concentration needed to inhibit yeast growth for different probabilities. The reduction of pH increased the number of combinations of a(w) and potassium sorbate concentration with probabilities to inhibit yeast growth higher than 0.95. With a probability of growth of 0.05 and using the logistic models, the critical pH values were higher for 50 h of incubation than those required for 350 h. With lower a(w) values and increasing potassium sorbate concentration the critical pH values increased. Logistic regression is a useful tool to evaluate the effects of the combined factors on microbial growth.

Food Microbiology↗

PubMed2000

Probabilistic modeling of single-trial fMRI data.

This paper describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.

Brain↗

PubMed2006

Risk assessment and food allergy: the probabilistic model applied to allergens.

In order to assess the risk of unintended exposure to food allergens, traditional deterministic risk assessment is usually applied, leading to inconsequential conclusions as 'an allergic reaction cannot be excluded'. TNO therefore developed a quantitative risk assessment model for allergens based on probabilistic techniques resulting in a more exhaustive risk assessment and more detailed information. By now, this approach is recognized as the future approach in allergen risk assessment. A case study (hazelnut proteins in chocolate spread) is presented as a proof of concept.

Allergens↗

PubMed2001

Tractable approximations for probabilistic models: the adaptive Thouless-Anderson-Palmer mean field approach.

We develop an advanced mean field method for approximating averages in probabilistic data models that is based on the Thouless-Anderson-Palmer (TAP) approach of disorder physics. In contrast to conventional TAP, where the knowledge of the distribution of couplings between the random variables is required, our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is so far restricted to models with nonglassy behavior, by replica calculations for a wide class of models as well as by simulations for a real data set.

Journal Article↗

PubMed2006

Recognition of dynamic video contents with global probabilistic models of visual motion.

The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos.

Algorithms↗

PubMed2002

The use of food consumption data in assessments of exposure to food chemicals including the application of probabilistic modelling.

Emphasis on public health and consumer protection, in combination with globalisation of the food market, has created a strong demand for exposure assessments of food chemicals. The food chemicals for which exposure assessments are required include food additives, pesticide residues, environmental contaminants, mycotoxins, novel food ingredients, packaging-material migrants, flavouring substances and nutrients. A wide range of methodologies exists for estimating exposure to food chemicals, and the method chosen for a particular exposure assessment is influenced by the nature of the chemical, the purpose of the assessment and the resources available. Sources of food consumption data currently used in exposure assessments range from food balance sheets to detailed food consumption surveys of individuals and duplicate-diet studies. The fitness-for-purpose of the data must be evaluated in the context of data quality and relevance to the assessment objective. Methods to combine the food consumption data with chemical concentration data may be deterministic or probabilistic. Deterministic methods estimate intakes of food chemicals that may occur in a population, but probabilistic methods provide the advantage of estimating the probability with which different levels of intake will occur. Probabilistic analysis permits the exposure assessor to model the variability (true heterogeneity) and uncertainty (lack of knowledge) that may exist in the exposure variables, including food consumption data, and thus to examine the full distribution of possible resulting exposures. Challenges for probabilistic modelling include the selection of appropriate modes of inputting food consumption data into the models.

Consumer Product Safety↗

PubMed1997

Constructing probabilistic models.

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas.

Algorithms↗

PubMed2005

Probabilistic model of the human protein-protein interaction network.

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Chromosome Mapping↗

PubMed2004

Presynaptic mechanisms of motor fluctuations in Parkinson's disease: a probabilistic model.

Levodopa-treated Parkinson's disease is often complicated by the occurrence of motor fluctuations, which can be predictable ('wearing-off') or unpredictable ('on-off'). In contrast, untreated dopa-responsive dystonia (DRD) is usually characterized by predictable diurnal fluctuation. The pathogenesis of motor fluctuations in treated Parkinson's disease and diurnal fluctuation in untreated DRD is poorly understood. We have developed a mathematical model indicating that all these fluctuations in motor function can be explained by presynaptic mechanisms. The model is predicated upon the release of dopamine being subject to probabilistic variations in the quantity of dopamine released by exocytosis of vesicles. Specifically, we propose that the concentration of intravesicular dopamine undergoes dynamic changes according to a log-normal distribution that is associated with different probabilities of release failure. Changes in two parameters, (i) the proportion of vesicles that undergo exocytosis per unit of time and (ii) the proportion of dopamine subject to re-uptake from the synapse, allowed us to model different curves of levodopa response, for the same degree of nigrostriatal damage in Parkinson's disease. The model predicts the following periods of levodopa clinical benefit: 4 h for stable responders, 3 h for wearing-off fluctuators, and 1.5 h for on-off fluctuators. The model also predicts that diurnal fluctuation in untreated DRD should occur some 8 h after getting up in the morning. All these results fit well with clinical observations. Additionally, we calculated the probability of obtaining a second ON period after a single dose of levodopa in Parkinson's disease (the 'yo-yoing' phenomenon). The model shows that the yo-yoing phenomenon depends on how fast the curve crosses the threshold that separates ON and OFF states, which explains why this phenomenon is virtually exclusive to patients with on-off fluctuations. The model supports the idea that presynaptic mechanisms play a key role in both short-duration and long-duration responses encountered in Parkinson's disease. Dyskinesias may also be explained by the same mechanisms.

Antiparkinson Agents↗

PubMed2005

[A probabilistic model of cardiac electrical activity based on a cellular automata system].

INTRODUCTION AND OBJECTIVES: Mathematical models of cardiac electrical activity may help to elucidate the electrophysiological mechanisms involved in the genesis of arrhythmias. The most realistic simulations are based on reaction-diffusion models and involve a considerable computational burden. The aim of this study was to develop a computer model of cardiac electrical activity able to simulate complex electrophysiological phenomena but free of the large computational demands required by other commonly used models. MATERIAL AND METHOD: A cellular automata system was used to model the cardiac tissue. Each individual unit had several discrete states that changed according to simple rules as a function of the previous state and the state of the neighboring cells. Activation was considered as a probabilistic process and was adjusted using restitution curves. In contrast, repolarization was modeled as a deterministic phenomenon. Cell currents in the model were calculated with a prototypical action potential that allowed virtual monopolar and bipolar electrograms to be simulated at any point in space. RESULTS: Reproducible flat activation fronts, propagation from a focal stimulus, and reentry processes that were stable and unstable in two dimensions (with their corresponding electrograms) were obtained. The model was particularly suitable for the simulation of the effects observed in curvilinear activation fronts. Fibrillatory conduction and stable rotors in two- and three-dimensional substrates were also obtained. CONCLUSIONS: The probabilistic cellular automata model was simple to implement and was not associated with a high computational burden. It provided a realistic simulation of complex phenomena of interest in electrophysiology.

Computer Simulation↗

PubMed1999

Probabilistic model for fluences and peak fluxes of solar energetic particles.

The model is intended for calculating the probability for solar energetic particles (SEP), i.e., protons and Z=2-28 ions, to have an effect on hardware and on biological and other objects in the space. The model describes the probability for the > or = 10 MeV/nucleon SEP fluences and peak fluxes to occur in the near-Earth space beyond the Earth magnetosphere under varying solar activity. The physical prerequisites of the model are as follows. 1. The occurrence of SEP is a probabilistic process. 2. The mean SEP occurrence frequency is a power-law function of solar activity (sunspot number). 3. The SEP size (taken to be the > or = 30 MeV proton fluence size) distribution is a power-law function within a 10(5)-10(11) proton/cm2 range. 4. The SEP event particle energy spectra are described by a common function whose parameters are distributed log-normally. 5. The SEP mean composition is energy-dependent and suffers fluctuations described by log-normal functions in separate events.

Cosmic Radiation↗

PubMed2004

An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images.

This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented.

Algorithms↗

PubMed2005

A probabilistic model of face mapping with local transformations and its application to person recognition.

This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations. The proposed system of local transformations and neighboring constraints is embedded within the probabilistic framework of a two-dimensional hidden Markov model. More specifically, we model two types of intraclass variabilities involving variations in facial expressions and illumination, respectively. The performance of the resulting method is assessed on a large data set consisting of four face databases. In particular, it is shown to outperform a leading approach to face recognition, namely, the Bayesian intra/extrapersonal classifier.

Algorithms↗

PubMed1997

Diagnostic boundaries, reasoning and depressive disorder, II. Application of a probabilistic model to the OPCS general population survey of psychiatric morbidity in Great Britain.

BACKGROUND: Reliable prevalence and risk estimation of psychiatric disorder is a cornerstone to achieving objectives in public health psychiatry. Research strategies have increasingly depended, therefore, upon the progressive evolution and refinement of diagnostic approaches designed to reflect better current knowledge concerning prognosis, course and outcome but essentially the need to improve agreement between users of the various schemes. METHODS: This paper contrasts a conventional with a probabilistic approach to the diagnosis of depression based upon the OPCS United Kingdom National survey of psychiatric morbidity. The probabilistic approach, while designed to mimic current diagnostic practice in relation to the depressive disorders, naturally includes provision for the allocation of respondents on a scale of diagnostic uncertainty according to the severity of their presenting condition. RESULTS: Findings are reported arising from the application of the probabilistic method to three areas of research interest in public health psychiatry, namely; an evaluation of additivity of event exposure and depressive morbidity, secondly use of the approach for investigating psychosocial models of depressive disorder and thirdly for assessing the agreement between depressive disorder when classified according to competing diagnostic schemes. CONCLUSIONS: The results show application of the probabilistic approach to provide a firm basis for achieving gains in both the stability and precision of risk profile estimation for depressive conditions.

Adolescent↗

PubMed1987

A probabilistic model of mosaicism based on the histological analysis of chimaeric rat liver.

The analysis of pattern development in mosaic and chimaeric animals has provided insight into a number of developmental problems. In order to aid the understanding of the dynamics of the development of mosaic tissues, a computer simulation of the generation of a mosaic tissue was created using simple probabilistic decisions. Results of quantitative analysis of the simulated mosaicism were compared with chimaeric liver. Chimaeric animals were produced by morula aggregation between histologically distinguishable strains of congenic rats. The livers of these animals revealed a pattern of patchy mosaicism unrelated to either acinar or lobular architecture of the organ. Independent quantifiable parameters were correlated and compared between the simulation and chimaeric liver tissue. This analysis showed that extensive cell migration is not required to develop finely variegated mosaic tissue and that the patterns of mosaicism observed could have resulted from tissue development in which as few as three reiterated decisions were required. First, the simulation established anlagen of two cell types of various specified proportions with randomly chosen placement. Second, in each generation of the simulation the order in which the cells divided was established randomly. Third, there was a random choice of the direction of placement of the daughter cell. The quantitative relationships between the proportion of cell types, the area of patches and the number of patches per unit area was consistent between the simulation and the chimaeric tissue.

Animals↗

PubMed2006

A probabilistic model for binaural sound localization.

This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to separately measure interaural time differences and interaural level differences for a number of frequencies and process these measurements as a whole. This leads to two-dimensional frequency versus time-delay representations of binaural cues, so-called activity maps. A probabilistic evaluation is presented to estimate the position of a sound source over time based on these activity maps. Learned reference maps for different azimuthal positions are integrated into the computation to gain time-dependent discrete conditional probabilities. At every timestep these probabilities are combined over frequencies and binaural cues to estimate the sound source position. In addition, they are propagated over time to improve position estimation. This leads to a system that is able to localize audible signals, for example human speech signals, even in reverberating environments.

Artificial Intelligence↗

PubMed2000

A practical application of probabilistic modelling in assessment of dietary exposure of fruit consumers to pesticide residues.

In 1996, studies on a range of organophosphate and carbamate pesticide residues in fruit that may be eaten as single items reported variability. The usual point estimate exposure model did not take account of the variation in residue levels between items or variation in consumption patterns of individual consumers. Using only the highest residue levels and consumption values for each of the multiple sources (different fruit) could lead to overestimates of residue intakes which would indicate higher than actual levels of risk. Probabilistic simulation was identified as a tool that could utilize all the available information from the variability studies and fruit consumption data collected from dietary surveys. The estimation of exposure of toddlers to carbaryl is shown as an example. The number of samples representing some combinations of fruit in the toddler dietary survey was particularly low and the validity of extrapolating from these was unknown. Therefore, consumption values were simulated using the data for frequency and amount eaten from the whole database. The data indicated that there were some weak positive associations between consumption levels of the different fruit. However, inclusion of correlated sampling in the model simulation was considered too conservative. The profiles of carbaryl residues in different retail batches differed. Therefore a model was constructed that differentiated between different residue profiles and sampled separate residue levels for each item assumed to be eaten. Two simpler models, both ignoring the effect of re-sampling from the same batch, were also used to estimate exposure. All three models were considered to give realistic views of the likely short-term intakes and the outputs were useful as an aid to decision-making in terms of necessary regulatory action.

Carbaryl↗

PubMed2006

A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.

BACKGROUND: Although microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected. RESULTS: With four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set included 119 samples, and the predictors were evaluated by strict leave-one-out cross validation. Trials included classical combinations, i.e., one-to-one, one-to-the-rest, but the predictor using more combination exhibited the better prediction accuracy. This characteristic was consistent with other gene expression data sets. The performance of the selected predictor was then tested with an independent set consisting of 49 samples. The resulting test prediction accuracy was 85.7%. CONCLUSION: Molecular diagnosis of thyroid tissues is feasible by gene expression profiling, and the current level is promising towards the automatic diagnostic tool to complement the present medical procedures. A multi-class predictor with an exhaustive combination of binary classifiers could achieve a higher prediction accuracy than those with classical combinations and other predictors such as multi-class SVM. The probabilistic outputs of the predictor offer more detailed information for each sample, which enables visualization of each sample in low-dimensional classification spaces. These new concepts should help to improve the multi-class classification including that of cancer tissues.

Algorithms↗