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At least 1,225 records · Page 68Linked to original sources
PubMed2004

Application of influence diagrams to prostate intensity-modulated radiation therapy plan selection.

The purpose is to incorporate clinically relevant factors such as patient-specific and dosimetric information as well as data from clinical trials in the decision-making process for the selection of prostate intensity-modulated radiation therapy (IMRT) plans. The approach is to incorporate the decision theoretic concept of an influence diagram into the solution of the multiobjective optimization inverse planning problem. A set of candidate IMRT plans was obtained by varying the importance factors for the planning target volume (PTV) and the organ-at-risk (OAR) in combination with simulated annealing to explore a large part of the solution space. The Pareto set for the PTV and OAR was analysed to demonstrate how the selection of the weighting factors influenced which part of the solution space was explored. An influence diagram based on a Bayesian network with 18 nodes was designed to model the decision process for plan selection. The model possessed nodes for clinical laboratory results, tumour grading, staging information, patient-specific information, dosimetric information, complications and survival statistics from clinical studies. A utility node was utilized for the decision-making process. The influence diagram successfully ranked the plans based on the available information. Sensitivity analyses were used to judge the reasonableness of the diagram and the results. In conclusion, influence diagrams lend themselves well to modelling the decision processes for IMRT plan selection. They provide an excellent means to incorporate the probabilistic nature of data and beliefs into one model. They also provide a means for introducing evidence-based medicine, in the form of results of clinical trials, into the decision-making process.

Aged↗
PubMed2006

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities.

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. Recent experimental high-throughput techniques, such as Chromatin Immunoprecipitation (ChIP) provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data. RESULTS: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast datasets in which the network structure has previously been obtained using ChIP data. 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

Algorithms↗
PubMed2002

Constraints on radiative forcing and future climate change from observations and climate model ensembles.

The assessment of uncertainties in global warming projections is often based on expert judgement, because a number of key variables in climate change are poorly quantified. In particular, the sensitivity of climate to changing greenhouse-gas concentrations in the atmosphere and the radiative forcing effects by aerosols are not well constrained, leading to large uncertainties in global warming simulations. Here we present a Monte Carlo approach to produce probabilistic climate projections, using a climate model of reduced complexity. The uncertainties in the input parameters and in the model itself are taken into account, and past observations of oceanic and atmospheric warming are used to constrain the range of realistic model responses. We obtain a probability density function for the present-day total radiative forcing, giving 1.4 to 2.4 W m-2 for the 5-95 per cent confidence range, narrowing the global-mean indirect aerosol effect to the range of 0 to -1.2 W m-2. Ensemble simulations for two illustrative emission scenarios suggest a 40 per cent probability that global-mean surface temperature increase will exceed the range predicted by the Intergovernmental Panel on Climate Change (IPCC), but only a 5 per cent probability that warming will fall below that range.

Journal Article↗
PubMed2003

Bayesian approaches to multiple sources of evidence and uncertainty in complex cost-effectiveness modelling.

Increasingly complex models are being used to evaluate the cost-effectiveness of medical interventions. We describe the multiple sources of uncertainty that are relevant to such models, and their relation to either probabilistic or deterministic sensitivity analysis. A Bayesian approach appears natural in this context. We explore how sensitivity analysis to patient heterogeneity and parameter uncertainty can be simultaneously investigated, and illustrate the necessary computation when expected costs and benefits can be calculated in closed form, such as in discrete-time discrete-state Markov models. Information about parameters can either be expressed as a prior distribution, or derived as a posterior distribution given a generalized synthesis of available data in which multiple sources of evidence can be differentially weighted according to their assumed quality. The resulting joint posterior distributions on costs and benefits can then provide inferences on incremental cost-effectiveness, best presented as posterior distributions over net-benefit and cost-effectiveness acceptability curves. These ideas are illustrated with a detailed running example concerning the cost-effectiveness of hip prostheses in different age-sex subgroups. All computations are carried out using freely available software for conducting Markov chain Monte Carlo analysis.

Adult↗
PubMed2001

Bayesian value-of-information analysis. An application to a policy model of Alzheimer's disease.

A framework is presented that distinguishes the conceptually separate decisions of which treatment strategy is optimal from the question of whether more information is required to inform this choice in the future. The authors argue that the choice of treatment strategy should be based on expected utility, and the only valid reason to characterize the uncertainty surrounding outcomes of interest is to establish the value of acquiring additional information. A Bayesian decision theoretic approach is demonstrated through a probabilistic analysis of a published policy model of Alzheimer's disease. The expected value of perfect information is estimated for the decision to adopt a new pharmaceutical for the population of patients with Alzheimer's disease in the United States. This provides an upper bound on the value of additional research. The value of information is also estimated for each of the model inputs. This analysis can focus future research by identifying those parameters where more precise estimates would be most valuable and indicating whether an experimental design would be required. We also discuss how this type of analysis can also be used to design experimental research efficiently (identifying optimal sample size and optimal sample allocation) based on the marginal cost and marginal benefit of sample information. Value-of-information analysis can provide a measure of the expected payoff from proposed research, which can be used to set priorities in research and development. It can also inform an efficient regulatory framework for new healthcare technologies: an analysis of the value of information would define when a claim for a new technology should be deemed substantiated and when evidence should be considered competent and reliable when it is not cost-effective to gather any more information.

Alzheimer Disease↗
PubMed2002

Mechanisms of long-distance dispersal of seeds by wind.

Long-distance dispersal (LDD) is central to species expansion following climate change, re-colonization of disturbed areas and control of pests. The current paradigm is that the frequency and spatial extent of LDD events are extremely difficult to predict. Here we show that mechanistic models coupling seed release and aerodynamics with turbulent transport processes provide accurate probabilistic descriptions of LDD of seeds by wind. The proposed model reliably predicts the vertical distribution of dispersed seeds of five tree species observed along a 45-m high tower in an eastern US deciduous forest. Simulations show that uplifting above the forest canopy is necessary and sufficient for LDD, hence, they provide the means to define LDD quantitatively rather than arbitrarily. Seed uplifting probability thus sets an upper bound on the probability of long-distance colonization. Uplifted yellow poplar seeds are on average lighter than seeds at the forest floor, but also include the heaviest seeds. Because uplifting probabilities are appreciable (as much as 1 5%), and tree seed crops are commonly massive, some LDD events will establish individuals that can critically affect plant dynamics on large scales.

Computer Simulation↗
PubMed2026

Accelerating inference in genomic and proteomic foundation models via speculative decoding.

MOTIVATION: Genomic and protein foundation models (GFMs and PFMs) have demonstrated strong performance in learning the language of DNA and proteins, but their use in large-scale sequence generation is limited by the latency of autoregressive decoding. Because every token triggers a forward pass of a large Transformer, whose inference is relatively slow, long-sequence generation quickly becomes costly. RESULTS: In this work we adapt speculative decoding to a representative GFM: the DNA model DNAGPT and two representative PFMs: ProGen2 and ProtGPT2. We implement a probabilistic variant of speculative decoding, in which a lightweight draft model proposes short token spans and a larger target model verifies or corrects them in parallel, while preserving the target model's sampling distribution. Across all three models we systematically study the effect of speculation window length, temperature, draft architecture and prompt length, and we benchmark tokens per second over multiple runs per configuration. Speculative decoding yields consistent speedups over standard key-value cached decoding, with maximum observed speedup reaching 100% increase, while average gains across models ranging between 20% and 40% (e.g. 1.2×-1.4×), without changing the underlying target model predictions. Our results show that speculative decoding is a practical and model-agnostic strategy for accelerating genomic and proteomic sequence generation without sacrificing prediction quality. AVAILABILITY AND IMPLEMENTATION: All code and results are freely available at https://github.com/Georgakopoulos-Soares-lab/BioSpecDec.

Genomics↗
PubMedDate not supplied

A practical sub-space adaptive filter.

A Sub-Space Adaptive Filter (SSAF) model is developed using, as a basis, the Modified Probabilistic Neural Network (MPNN) and its extension the Tuneable Approximate Piecewise Linear Regression (TAPLR) model. The TAPLR model can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each sub-space to the best approximately piecewise linear model over the whole data space. A suitable value in between ensures that all neighbouring piecewise linear models merge together smoothly at their boundaries. This model was developed by altering the form of the MPNN, a network used for general nonlinear regression. The MPNNs special structure allows it to be easily used to model a process by appropriately weighting piecewise linear models associated with each of the network's radial basis functions. The model has now been further extended to allow each piecewise linear model section to be adapted separately as new data flows through it. By doing this, the proposed SSAF model represents a learning/filtering method for nonlinear processes that provides one solution to the stability/plasticity dilemma associated with standard adaptive filters.

Linear Models↗
PubMed1980

[Dynamics of systems with induced cell proliferation within the framework of a branching stochastic process model. I. The number of cell generations induced to proliferate].

The probabilistic description of cell generation numbers in the populations induced to proliferate is considered on the basis of the model of branching age-dependent stochastic process. The recurrent formulas for generating functions (and moments) are derived for the following process which are of interest for the analysis of induced proliferation in closed cell populations: the number of cells in n-th generation horn up to the moment of observation, and the number of cell in n-th generation existing at a given moment.

Cell Division↗
PubMed2001

Fatigue of restorative materials.

Failure due to fatigue manifests itself in dental prostheses and restorations as wear, fractured margins, delaminated coatings, and bulk fracture. Mechanisms responsible for fatigue-induced failure depend on material ductility: Brittle materials are susceptible to catastrophic failure, while ductile materials utilize their plasticity to reduce stress concentrations at the crack tip. Because of the expense associated with the replacement of failed restorations, there is a strong desire on the part of basic scientists and clinicians to evaluate the resistance of materials to fatigue in laboratory tests. Test variables include fatigue-loading mode and test environment, such as soaking in water. The outcome variable is typically fracture strength, and these data typically fit the Weibull distribution. Analysis of fatigue data permits predictive inferences to be made concerning the survival of structures fabricated from restorative materials under specified loading conditions. Although many dental-restorative materials are routinely evaluated, only limited use has been made of fatigue data collected in vitro: Wear of materials and the survival of porcelain restorations has been modeled by both fracture mechanics and probabilistic approaches. A need still exists for a clinical failure database and for the development of valid test methods for the evaluation of composite materials.

Compomers↗
PubMed2000

A probabilistic description of radioactive contamination.

A discrete probability model is used to aid in the description of gamma-ray spectroscopic data obtained from a radioactively contaminated territory. The approach gives a natural probability for distinguishing contaminated regions and is useful for describing spatial vector valued discrete random fields. Using the simplest variant of this model, a part of the territory adjoining the former Semipalatinsk's nuclear test site in Kazakhstan has been investigated. Probabilities of exceeding the safety limits for 137Cs and integral gamma-radiation have been estimated.

Gamma Rays↗
PubMed2005

Predicting methyl tert-butyl ether, tert-butyl formate, and tert-butyl alcohol levels in the environment using the fugacity approach.

Through its extensive use as a fuel oxygenate, methyl tert-butyl ether (MTBE) is found nearly ubiquitouslythroughout the environment. To better understand the environmental fate of MTBE, fugacity models are commonly used. However, models developed by the scientific community and by governmental bodies differ in their predictions of relative MTBE concentrations for relevant environmental compartments and of seasonal concentration variations; further, to date they have not considered the formation of transformation products. In this study, the sensitivity of predicted environmental concentrations of MTBE and its two major degradation products, tert-butyl formate (TBF) and tert-butyl alcohol (TBA), to all types of model input parameters is analyzed in a probabilistic sensitivity analysis. This analysis allowed for an assessment of the most influential parameters for predicting soil, water, and air concentrations and thereby provided insight into why previous modeling studies on MTBE differed. Further, the information from the sensitivity analysis was used to parametrize a multispecies transformation model for predicting European concentration levels of MTBE and, for the first time, TBF and TBA. Water and air concentrations of MTBE predicted with the transformation model were in good agreement with measurements of environmental samples. No studies are available on environmental TBF and TBA levels to compare with model predictions; however, the modeling results indicate that, in the water phase, TBA concentrations may reach appreciable levels. One major uncertainty identified regarding the prediction of TBA levels was the fraction of TBA formed from atmospheric MTBE and TBF.

Air Pollutants↗
PubMed1995

Probabilistic secretion of quanta at somatic motor-nerve terminals: the fusion-pore model, quantal detection and autoinhibition.

The probability of detecting first, second, and later quanta secreted at release sites of a motor-nerve terminal during the early release period following a nerve impulse has been addressed. The possibility that early quantal release autoinhibits later quantal release during this period has also been ascertained. In this investigation, a model for the secretion of a quantum at a release site is developed in which, following the influx and diffusion of calcium ions to a release site protein associated with synaptic vesicles, kappa steps of association of the ions with the protein then occur at rate alpha. The release site protein then undergoes a conformational change which may not go on to completion if calcium ions dissociate from the protein at rate gamma. If this process does reach completion then a fusion-pore between the vesicle and the presynaptic membrane is created; this happens at rate delta. Key assumptions of this fusion-pore model are that the quantal secretions from each site are independent of each other, and that there is a large number of vesicles, each with a small probability of secretion, so that the number of secretions is Poisson in nature. These assumptions allow analytical expressions to be obtained for predicting the times at which first, second and later quanta are secreted during the early release period following an impulse. To test the model, experiments were performed in which the times of first, second and later quantal releases were determined at discrete regions along the length of visualized motor-terminal branches in toad (Bufo marinus) muscles. Estimates of model rate constants and of kappa from the times for first quantal secretions failed to give satisfactory predictions of the observed times of later secretions. Therefore, either the model fails, or the procedure used for detecting later quantal events as a consequence of their being masked by earlier quantal events is inadequate. To solve this detection problem, a two-dimensional analysis of the spread of charge following the secretion of a quantum at a random site on the motor-terminal branch has been done. This allows determination of the probability that later quanta will be detected following secretion of earlier quanta. The detection model was then incorporated into the fusion-pore model to predict the times at which second and later quanta occur during the early release period, based on the estimates of the model parameters derived from the analysis of first quantal releases. Good estimates were now obtained for the observed times of second and later quantal releases, indicating that appropriate procedures must be adopted for adequate detection of quantal secretions. Furthermore, the experiments provide support for the fusion-pore model. It has been suggested that the binomial nature of quantal release from the entire motor-nerve terminal may be explained if early quantal release inhibits later quantal release during the early quantal release phase (M. R. Bennett & J. Robinson 1990, Proc. R. Soc. Lond. B 239, 329-358). Although the fusion-pore detection error model gave good predictions of the observed times of first, second and later quantal releases, these may be improved if a model for autoinhibition is included. In this model the first quantum was taken as giving rise to an inhibition of secretion that propagates to surrounding release sites with a constant velocity, v. A combined model incorporating the fusion-pore detection error model and that for autoinhibition was then used to predict second and later quantal latencies, by using the first quantal latencies to determine the estimates for the parameters in the combined model. When this analysis was done on the times for quantal secretion at sites on thirteen different motor-nerve terminals, the value of v was estimated as zero in each case, so that no autoinhibitory effect was observed.

Animals↗
PubMed2004

Probabilistic sequential independent components analysis.

Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets.

Algorithms↗
PubMed2000

Bubonic plague: a metapopulation model of a zoonosis.

Bubonic plague (Yersinia pestis) is generally thought of as a historical disease; however, it is still responsible for around 1000-3000 deaths each year worldwide. This paper expands the analysis of a model for bubonic plague that encompasses the disease dynamics in rat, flea and human populations. Some key variables of the deterministic model, including the force of infection to humans, are shown to be robust to changes in the basic parameters, although variation in the flea searching efficiency, and the movement rates of rats and fleas will be considered throughout the paper. The stochastic behaviour of the corresponding metapopulation model is discussed, with attention focused on the dynamics of rats and the force of infection at the local spatial scale. Short-lived local epidemics in rats govern the invasion of the disease and produce an irregular pattern of human cases similar to those observed. However, the endemic behaviour in a few rat subpopulations allows the disease to persist for many years. This spatial stochastic model is also used to identify the criteria for the spread to human populations in terms of the rat density. Finally, the full stochastic model is reduced to the form of a probabilistic cellular automaton, which allows the analysis of a large number of replicated epidemics in large populations. This simplified model enables us to analyse the spatial properties of rat epidemics and the effects of movement rates, and also to test whether the emergent metapopulation behaviour is a property of the local dynamics rather than the precise details of the model.

Animals↗
PubMed2005

Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism.

Dopamine (DA) depletion in the basal ganglia (BG) of Parkinson's patients gives rise to both frontal-like and implicit learning impairments. Dopaminergic medication alleviates some cognitive deficits but impairs those that depend on intact areas of the BG, apparently due to DA ''overdose.'' These findings are difficult to accommodate with verbal theories of BG/DA function, owing to complexity of system dynamics: DA dynamically modulates function in the BG, which is itself a modulatory system. This article presents a neural network model that instantiates key biological properties and provides insight into the underlying role of DA in the BG during learning and execution of cognitive tasks. Specifically, the BG modulates the execution of ''actions'' (e.g., motor different parts of the frontal cortex. Phasic changes in DA, which occur during error feedback, dynamically modulate the BG threshold for facilitating/suppressing a cortical command in response to particular stimuli. Reduced dynamic range of DA explains Parkinson and DA overdose deficits with a single underlying dysfunction, despite overall differences in raw DA levels. Simulated Parkinsonism and medication effects provide a theoretical basis for behavioral data in probabilistic classification and reversal tasks. The model also provides novel testable predictions for neuropsychological and pharmacological studies, and motivates further investigation of BG/DA interactions with the prefrontal cortex in working memory.

Basal Ganglia↗
PubMed2003

Construction of genetic network using evolutionary algorithm and combined fitness function.

This paper proposes a method to capture the dynamics in gene expression data using S-system formalism and construct genetic network models. Our purposed method exploits the probabilistic heuristic search and divide-and-conquer approach to estimate the network structure. In evaluating the network structure, we attempt a primitive integration of other knowledge to the statistical criterion. The Z-score is used to analyze the robust and significant parameters from stochastic search results. We evaluated the proposed method on artificially generated data and E.coli mRNA expression data.

Algorithms↗
PubMed2005

Rebuilt AAA + motors reveal operating principles for ATP-fuelled machines.

Hexameric ring-shaped ATPases of the AAA + (for ATPases associated with various cellular activities) superfamily power cellular processes in which macromolecular structures and complexes are dismantled or denatured, but the mechanisms used by these machine-like enzymes are poorly understood. By covalently linking active and inactive subunits of the ATPase ClpX to form hexamers, here we show that diverse geometric arrangements can support the enzymatic unfolding of protein substrates and translocation of the denatured polypeptide into the ClpP peptidase for degradation. These studies indicate that the ClpX power stroke is generated by ATP hydrolysis in a single subunit, rule out concerted and strict sequential ATP hydrolysis models, and provide evidence for a probabilistic sequence of nucleotide hydrolysis. This mechanism would allow any ClpX subunit in contact with a translocating polypeptide to hydrolyse ATP to drive substrate spooling into ClpP, and would prevent stalling if one subunit failed to bind or hydrolyse ATP. Energy-dependent machines with highly diverse quaternary architectures and molecular functions could operate by similar asymmetric mechanisms.

Adenosine Triphosphatases↗