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Results for “probabilistic modelling”

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PubMed2004

Probabilistic kinetic model of slow oxidation of low-density lipoprotein: I. Theory.

The microscopic probabilistic model has been introduced to explain the kinetics of very slow oxidation of low-density lipoprotein (LDL) from human plasma. The LDL oxidation, carried out in very unfavorable conditions, is assumed to be initiated by the traces of the transition-metal ions associated with the lipoprotein. The substrates for the metal-ion attack are alpha-tocopherol and the pre-formed lipid hydroperoxide. The theory assumes oscillation of the metal ions and alpha-tocopherol from the oxidized to the reduced states. In this model alpha-tocopherol acts as a pro-oxidant. The entire oxidation process consists of rare bursts of events in individual LDL particles. The reactions within the particles are treated in terms of probabilities of individual active species to participate in a specified reaction. The circular flow of the radical reactions could be visualized as circular flow of microscopic probabilities. The empirical, macroscopic quantities are quantitatively related with the microscopic probabilities, determined by a set of five adjustable parameters. The differential equations describing the initial radical generation rate and the rates of change of concentration of oxygen, hydroperoxide, co-antioxidant and trapped radicals in an LDL system are numerically solved in a finite difference approach.

Free Radicals↗

PubMed1996

Learning temporal probabilistic causal models from longitudinal data.

Medical problems often require the analysis and interpretation of large collections of longitudinal data in terms of a structural model of the underlying physiological behavior. A suitable way to deal with this problem is to identify a temporal causal model that may effectively explain the patterns observed in the data. Here we will concentrate on probabilistic models, that provide a convenient framework to represent and manage underspecified information; in particular, we will consider the class of Causal Probabilistic Networks (CPN). We propose a method to perform structural learning of CPNs representing time-series through model selection. Starting from a set of plausible causal structures and a collection of possibly incomplete longitudinal data, we apply a learning algorithm to extract from the data the conditional probabilities describing each model. The models are then ranked according to their performance in reconstructing the original time-series, using several scoring functions, based on one-step ahead predictions. In this paper we describe the proposed methodology through an example taken from the diabetes monitoring domain. The selection process is applied to a set of input-output models that generalize the class of ARX models, where the inputs are the insulin and meal intakes and the outputs are the blood glucose levels. Although the physiological process underlying this particular application is characterized by strong non-linearities and low data reliability, we show that it is possible to obtain meaningful results, in terms of conditional probability learning and model ranking power.

Algorithms↗

PubMed2002

A probabilistic transmission model of Salmonella in the primary broiler production chain.

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%-17.4%] (no intervention), and [0.9%-5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%-43.1%] and [1.0%-5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.

Animals↗

PubMed2006

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription.

MOTIVATION: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors' expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes. RESULTS: We introduce a novel probabilistic model to infer transcription factor activities from microarray data when the structure of the regulatory network is known. The model is based on regression, retaining the computational efficiency to allow genome-wide investigation, but is rendered more flexible by sampling regression coefficients independently for each gene. This allows us to determine the strength with which a transcription factor regulates each of its target genes, therefore providing a quantitative description of the transcriptional regulatory network. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates of the activities. We demonstrate our model on two yeast datasets. In both cases the network structure was obtained using chromatin immunoprecipitation data. We show how predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma.

Computer Simulation↗

PubMed2000

Probabilistic Mental Models with Continuous Predictors.

Gigerenzer and his colleagues have sought to develop psychologically plausible models of human judgment. Their models are classified as ones of bounded rationality based on a principle of one-reason decision making. The models associated with the theory of Probabilistic Mental Models (PMM) have been developed for tasks in which all predictors are binary. This article extends PMM to the case of continuous predictors. The current model employs the limitation on the number of categories people use in making absolute judgments along a single perceptual dimension (7 +/- 2; Miller, 1956). The algorithm transforms each continuous predictor to be consistent with this limitation, then implements a step-down one-reason decision procedure similar to previous PMM models. Like previous PMM models, the 7 +/- 2 model predicts binary judgments as well as a multiple-regression model. However, the model does not successfully predict the probability judgments of individual participants, which is also true of all other models in the literature. Copyright 2000 Academic Press.

Journal Article↗

PubMed2001

Viral burden in genital secretions determines male-to-female sexual transmission of HIV-1: a probabilistic empiric model.

OBJECTIVE: To develop a model to predict transmission of HIV-1 from men to women. DESIGN: HIV-1 in seminal plasma, and endocervical CCR5 receptors were correlated with epidemiological studies of HIV-1 transmission to develop a probabilistic model. SETTINGS: Semen samples were collected from patient subjects in Seattle Washington, Chapel Hill, North Carolina, and St. Gallen, Switzerland. Endocervical biopsy specimens were obtained from women in Chicago, Illinois. PARTICIPANTS: Eighty-six men (not receiving antiretroviral therapy) in whom CD4 cell count and semen volume were available, and 24 women in whom the number of endocervical CCR5 receptors were determined. MAIN OUTCOME MEASURES: Prediction of transmission of HIV-1 from men to women per episode of vaginal intercourse based on the absolute burden of HIV (volume x HIV RNA copies/ml seminal plasma). RESULTS: The model suggests efficient heterosexual transmission of HIV-1 when semen viral burden is high. When semen contains 100 000 copies of non-syncytium-inducing (NSI) HIV RNA the probability of HIV-1 transmission is 1 per 100 episodes of intercourse; conversely, with 1000 copies NSI HIV RNA in semen, transmission probability is 3 per 10 000 episodes of intercourse. CONCLUSIONS: This model links biological and epidemiological data related to heterosexual HIV-1 transmission. The model can be used to estimate transmission of HIV from men with high semen viral burden from inflammation, or reduced burden after antiretroviral therapy. The results offer a biological explanation for the magnitude of the HIV epidemic in places where earlier studies have shown men have high semen viral burden, such as in sub-Saharan Africa. The model can be used to develop and test HIV-1 prevention strategies.

Cervix Uteri↗

PubMedDate not supplied

[Probabilistic cost-effectiveness analysis of the treatment of sleep apnea].

OBJECTIVE: To describe the application of a probabilistic cost-effectiveness analysis to nasal continuous positive airway passage (nCPAP) treatment of obstructive sleep apnea syndrome (OSAS). MATERIAL AND METHODS: The probabilistic model was constructed from a discrete Markov model. This probabilistic approach is characterized by the introduction of variables as probability distributions. The model performed 2,000 Monte Carlo simulations, and incremental costs and effectiveness were calculated in each. The results were analyzed through the cost-effectiveness plane, the acceptability curve, the net benefit rule, and the expected value of perfect information (EVPI). RESULTS: The mean cost-effectiveness ratio for nCPAP treatment was 5,480 Euro/QALY (quality-adjusted life year). Using an acceptability threshold of 30,000 Euro/QALY, the probabilistic analysis showed that nCPAP was the optimal treatment in 98.5% of the simulations. The EVPI showed that the parameter causing greatest uncertainty in the final results was the quality of life gain through nCPAP treatment. CONCLUSIONS: The results of our probabilistic analysis are endorsed by previous deterministic studies confirming that nCPAP treatment of OSAS is the most cost-effective strategy. An additional advantage of probabilistic analysis is that it allows uncertainty to be quantified; in the present case the probability of making the wrong decision was below 5%. Furthermore, this study reveals that to reduce uncertainty, research should center on improving information on quality of life.

Continuous Positive Airway Pressure↗

PubMed2005

A probabilistic screening model for evaluating pyrethroid surface water monitoring data.

Synthetic pyrethroids have been detected in recent California surface water monitoring. Filtration is avoided during sample workup because pyrethroids are extremely hydrophobic, tending to sorb to most surfaces. The resultant analytical pyrethroid concentrations reflect both dissolved pyrethroid and pyrethroid associated with suspended sediment in the water column. Such "whole-water" analytical data are not directly comparable to aquatic acute toxicity effect concentrations measured in laboratory sediment-free water. Consequently, any potential aquatic toxicity risk is indeterminate. In this study a simple probabilistic model was developed to allow a screening-level assessment of pyrethroid whole-water monitoring data. The results suggest that water column toxicity of pyrethroids is possible in California's agriculturally-dominated tributaries, and indicate that additional monitoring to better characterize pyrethroid water-column concentrations are warranted. Model refinement will depend on future work that more firmly establishes the relationship between pyrethroid partitioning and bioavailability, and that addresses the potential influence of dissolved organic carbon on pyrethroid sorption and bioavailability.

Adsorption↗

PubMed2002

Solvable Markov random field model in color image restoration.

We propose a scheme for image restoration of full color images by means of a solvable probabilistic model in the red-green-blue space. A special case of our solvable probabilistic model is equivalent to a multicomponent Gaussian model in the statistical mechanics. Exact closed expressions of the evidence and the expectation value of intensity at each pixel in our solvable probabilistic model can be obtained by using multidimensional Gaussian integral formulas and a discrete Fourier transform. In the present paper, the degradation process is assumed to be an additive white Gaussian noise. Hyperparameters are determined so as to maximize the evidence that is expressed in terms of the partition function in our solvable probabilistic model. This work is a pioneering work for the Bayesian approach to the color image restoration by means of the statistical-mechanical technique.

Journal Article↗

PubMed2005

Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models.

In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).

Algorithms↗

PubMed2005

New probabilistic graphical models for genetic regulatory networks studies.

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

Algorithms↗

PubMed1996

Random Utility Representations of Finite m-ary Relations

Block and Marschak (1960, in Olkin et al. (Eds.), Contributions to probability and statistics (pp. 97-132). Stanford, CA: Stanford Univ. Press) discussed the relationship between a probability distribution over the strict linear rankings on a finite set C and a family of jointly distributed random variables indexed by C. The present paper generalizes the concept of random variable (random utility) representations to m-ary relations. It specifies conditions on a finite family of random variables that are sufficient to construct a probability distribution on a given collection of m-ary relations over the family's index set. Conversely, conditions are presented for a probability distribution on a collection of m-ary relations over a finite set C to induce (on a given sample space) a family of jointly distributed random variables indexed by C. Four random variable representations are discussed as illustrations of the general method. These are a semiorder model of approval voting, a probabilistic model for betweenness in magnitude judgments, a probabilistic model for political ranking data, and a probabilistic concatenation describing certainty equivalents for the joint receipt of gambles. The main theorems are compared to related results of Heyer and Niederee (1989, in E. E. Roskam (Ed.), Mathematical psychology in progress (pp. 99-112). Berlin: Springer-Verlag; 1992, Mathematical Social Sciences, 23, 31-44).

Journal Article↗

PubMed2005

PGMC: a framework for probabilistic graphic model combination.

Decision making in biomedicine often involves incorporating new evidences into existing or working models reflecting the decision problems at hand. We propose a new framework that facilitates effective and incremental integration of multiple probabilistic graphical models. The proposed framework aims to minimize time and effort required to customize and extend the original models through preserving the conditional independence relationships inherent in two types of probabilistic graphical models: Bayesian networks and influence diagrams. We present a four-step algorithm to systematically combine the qualitative and the quantitative parts of the different models; we also describe three heuristic methods for target variable generation to reduce the complexity of the integrated models. Preliminary results from a case study in heart disease diagnosis demonstrate the feasibility and potential for applying the proposed framework in real applications.

Algorithms↗

PubMed2006

A decision support system for managing forest fire casualties.

Southern Europe is exposed to anthropogenic and natural forest fires. These result in loss of lives, goods and infrastructure, but also deteriorate the natural environment and degrade ecosystems. The early detection and combating of such catastrophes requires the use of a decision support system (DSS) for emergency management. The current literature reports on a series of efforts aimed to deliver DSSs for the management of the forest fires by utilising technologies like remote sensing and geographical information systems (GIS), yet no integrated system exists. This manuscript presents the results of scientific research aiming to the development of a DSS for managing forest fires. The system provides a series of software tools for the assessment of the propagation and combating of forest fires based on Arc/Info, ArcView, Arc Spatial Analyst, Arc Avenue, and Visual C++ technologies. The system integrates GIS technologies under the same data environment and utilises a common user interface to produce an integrated computer system based on semi-automatic satellite image processing (fuel maps), socio-economic risk modelling and probabilistic models that would serve as a useful tool for forest fire prevention, planning and management. Its performance has been demonstrated via real time up-to-date accurate information on the position and evolution of the fire. The system can assist emergency assessment, management and combating of the incident. A site demonstration and validation has been accomplished for the island of Evoia, Greece, an area particularly vulnerable to forest fires due to its ecological characteristics and prevailing wind patterns.

Decision Support Techniques↗

PubMed2005

Long-range spatial integration across contrast signs: a probabilistic mechanism?

It is widely assumed that the integration of orientation contrast across spatial gaps within the long-range regime is not selective to the contrast sign of the individual stimuli. Probabilistic models of perceptual integration, however, suggest that long-range spatial integration should be, if not selective, at least sensitive to local contrast signs. To clarify this issue, we tested predictions of a model based on conditional probabilistic weights of identical and opposite contrast signs in a simple spatial configuration of two co-linear lines. Contrast detection thresholds of the target line presented either by itself (control condition) or simultaneously with the co-linear inducer (test condition) were measured. The contrast sign of targets and inducers was varied so that all four possible combinations of signs were produced in the test conditions: (1) dark target with dark inducer, (2) dark target with bright inducer, (3) bright target with bright inducer and (4) bright target with dark inducer. The contrast intensity (Weber ratio) of dark and bright inducers was identical. The coaxial distance between target and inducer was constant in each of two experiments, testing for two distances that corresponded to an angular separation within the long-range domain of spatial integration as defined previously. It is found that targets and inducers with identical contrast signs produce significantly stronger facilitating effects on detection than stimuli with opposite signs. The data closely match predictions consistent with those of a probabilistic model of line contrast integration across spatial gaps and contrast signs within the long-range regime.

Adult↗

PubMed1997

Information retrieval: an overview of system characteristics.

The paper gives an overview of characteristics of information retrieval (IR) systems. The characteristics are identified from the descriptions of 23 IR systems. Four IR models are discussed: the Boolean model, the vector model, the probabilistic model and the connectionistic model. Twelve other characteristics of IR models are identified: search intermediary, domain knowledge, relevance feedback, natural language interface, graphical query language, conceptual queries, full-text IR, field searching, fuzzy queries, hypertext integration, machine learning, and ranked output. Finally, the relevance of IR systems for the World Wide Web is established.

Algorithms↗

PubMed2005

AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints.

We present a WWW server for AUGUSTUS, a software for gene prediction in eukaryotic genomic sequences that is based on a generalized hidden Markov model, a probabilistic model of a sequence and its gene structure. The web server allows the user to impose constraints on the predicted gene structure. A constraint can specify the position of a splice site, a translation initiation site or a stop codon. Furthermore, it is possible to specify the position of known exons and intervals that are known to be exonic or intronic sequence. The number of constraints is arbitrary and constraints can be combined in order to pin down larger parts of the predicted gene structure. The result then is the most likely gene structure that complies with all given user constraints, if such a gene structure exists. The specification of constraints is useful when part of the gene structure is known, e.g. by expressed sequence tag or protein sequence alignments, or if the user wants to change the default prediction. The web interface and the downloadable stand-alone program are available free of charge at http://augustus.gobics.de/submission.

Animals↗

PubMed2006

AUGUSTUS: ab initio prediction of alternative transcripts.

AUGUSTUS is a software tool for gene prediction in eukaryotes based on a Generalized Hidden Markov Model, a probabilistic model of a sequence and its gene structure. Like most existing gene finders, the first version of AUGUSTUS returned one transcript per predicted gene and ignored the phenomenon of alternative splicing. Herein, we present a WWW server for an extended version of AUGUSTUS that is able to predict multiple splice variants. To our knowledge, this is the first ab initio gene finder that can predict multiple transcripts. In addition, we offer a motif searching facility, where user-defined regular expressions can be searched against putative proteins encoded by the predicted genes. The AUGUSTUS web interface and the downloadable open-source stand-alone program are freely available from http://augustus.gobics.de.

Alternative Splicing↗