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Results for “probabilistic modelling”

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At least 379 records · Page 21Linked to original sources
PubMed2006

Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian community: the InterVA model.

OBJECTIVE: Verbal autopsy (VA) -- the interviewing of family members or caregivers about the circumstances of a death after the event -- is an established tool in areas where routine death registration is non-existent or inadequate. We assessed the performance of a probabilistic model (InterVA) for interpreting community-based VA interviews, in order to investigate patterns of cause-specific mortality in a rural Ethiopian community. We compared results with those obtained after review of the VA by local physicians, with a view to validating the model as a community-based tool. METHODS: Two-hundred and eighty-nine VA interviews were successfully completed; these included most deaths occurring in a defined community over a 1-year period. The VA interviews were interpreted by physicians and by the model, and cause-specific mortality fractions were derived for the whole community and for particular age groups using both approaches. FINDINGS: The results of the two approaches to interpretation correlated well in this example from Ethiopia. Four major cause groups accounted for over 60% of all mortality, and patterns within specific age groups were consistent with expectations for an underdeveloped high-mortality community in sub-Saharan Africa. CONCLUSION: Compared with interpretation by physicians, the InterVA model is much less labour intensive and offers 100% consistency. It is a valuable new tool for characterizing patterns of cause-specific mortality in communities without death registration and for comparing patterns of mortality in different populations.

Adolescent↗
PubMed2001

A model of microbial contamination of a water reservoir.

A three year record of daily fecal coliform counts in a Massachusetts water reservoir has the appearance of an irregular time series punctuated by outbursts of varying duration. The pattern is described in terms of a probabilistic model where the fluctuations in the 'regular' and 'explosive' regimes are governed by two sets of probabilities. It has been assumed that the random oscillations has a lognormal distribution, and that once an explosion threshold has been exceeded the increments or decrements in the population size have fixed probability distributions. The threshold for triggering an outburst was estimated by examining the randomness of the autocorrelation function of the record after it is filtered to eliminate peaks of progressively increasing magnitude. Once the threshold has been identified, the mean and standard deviation of the underlying lognormal distribution could be estimated directly from remains found in the record after all the peaks were removed. The probabilities of an increment and decrement during the outbursts and their relative magnitudes could also be estimated using simple formulas. These estimated parameter values were then used to generate realistic records with known threshold levels, which were subsequently used to assess the procedure's feasibility and sensitivity.

Computer Simulation↗
PubMed1984

Analysis of a cell cycle model based on unequal division of metabolic constituents to daughter cells during cytokinesis.

We demonstrate that the unequal division of RNA during cytokinesis explains the dispersion of cell generation times in CHO cell cultures. Experimental cytometric results reported previously serve as a basis for a probabilistic model of cytokinesis. Unequal RNA division to daughter cells, together with two simple laws of RNA production, are used as a source of randomness within the cell cycle. The model reproduces the experimental growth of the CHO cell population, including the observed variability in RNA content. The model has stabilizing properties which explain why a cell population with increased RNA content characteristics, a few cell cycles, to the original pattern. Other cell cycle characteristics, like sister-to-sister and mother-to-daughter generation time correlations implied by the model, are close to their experimental analogs. The conceptual basis of the model is general enough to include unequal division of factors other than RNA (cell mass, cell proteins, etc.) as sources of generation time variability. It seems that the observed dispersion of cell generation times, explained previously in the terms of random transitions in some part of the cell cycle (the Smith & Martin A and B state hypothesis), can be reduced to the single random event of unequal division. This supplies a new convenient tool in the investigation of cell cycle kinetics.

Animals↗
PubMed1995

Logarithmic growth in surface adsorption.

A review is made of experimental data on surface adsorption of particles and polymers from water solutions, their analysis and interpretation in terms of general theoretical models of surface adsorption. A characteristic isotherm and kinetics is found and defined as logistic growth. The discussion is focused on literature indicating a possibility of describing logistic growth by using statistical (probabilistic) models based on the mean stay time of molecules on the surface. The statistical approach is further elaborated as follows: ligands arriving at a surface have a binary choice-to bind or to become reflected. Since the number of attempts to bind, n, will be high we can use the true mean of the binomial distribution to describe the reaction and write: S = n a; where S is the number of successful attempts and a is the probability of binding. The probability, a, will depend on the site density and on the sticking probability of the ligand at the binding site. Several experimental studies show that surface reactions have a nonlinear time- and concentration dependence and can be described by a Boltzmann factor of the form, I(1-e-t/tau); where I is the flux of ligands to the surface and tau = stay-time. The exponential form indicates that the reactions are self-dependent, and a statistical model for description of such reactions will be of the form: S(t) = N(o)(1-2-alpha t) (2-beta t); where N(o) is the number of molecules present in the system, alpha relates to the probability of positive cooperativity or t-dependent binding, and beta relates to the probability of desorption.

Adsorption↗
PubMed2000

Performance of APACHE III models in an Australian ICU.

STUDY OBJECTIVE: Evaluation of the performance of the APACHE (acute physiology and chronic health evaluation) III ICU and hospital mortality models at an Australian tertiary adult ICU. DESIGN: Noninterventional, observational study. SETTING: Metropolitan, Australian, tertiary referral medical/surgical ICU. PATIENTS: A total of 3,398 consecutive eligible admissions from January 1, 1995, to December 31, 1997. MEASUREMENTS: Prospective collection of demographic, diagnostic, physiologic, laboratory, admission, and discharge data. RESULTS: The patient sample was younger and more commonly male, with more comorbidities and a different operative and referral source mix, compared to the APACHE III development sample. Receiver operating characteristic curve areas for ICU (0.92) and hospital mortality (0.90) demonstrated excellent discrimination. Observed ICU mortality (9.9%) did not significantly differ from the prediction of the APACHE III model (8.9%) or the APACHE III model adjusted for hospital characteristics (10.5%). The hospital mortality (16.0%) was underestimated by the APACHE III model [13.6%; chi(2)(1) = 7.4; p = 0.01]. With proprietary adjustments for hospital characteristics (14.9%) or referenced to the US database (15.6%), agreement was closer. Good calibration was found with all models except the unadjusted hospital mortality model. CONCLUSION: In contrast to other non-American studies, this Australian study demonstrates that the APACHE III can perform well on independent assessment. As perfect discrimination and calibration cannot coexist in a probabilistic model with dichotomous outcomes, performance of APACHE III models with proprietary adjustment for hospital characteristic provide a good compromise for use in quality surveillance.

APACHE↗
PubMed2004

Sequence-independent segmentation of magnetic resonance images.

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.

Algorithms↗
PubMed2005

Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation.

Decomposing transcriptional regulatory networks into functional modules and determining logical relations between them is the first step toward understanding transcriptional regulation at the system level. Modules based on analysis of genome-scale data can serve as the basis for inferring combinatorial regulation and for building mathematical models to quantitatively describe the behavior of the networks. We present here an algorithm called modem to identify target genes of a transcription factor (TF) from a single expression experiment, based on a joint probabilistic model for promoter sequence and gene expression data. We show how this method can facilitate the discovery of specific instances of combinatorial regulation and illustrate this for a specific case of transcriptional networks that regulate sporulation in the yeast Saccharomyces cerevisiae. Applying this method to analyze two crucial TFs in sporulation, Ndt80p and Sum1p, we were able to delineate their overlapping binding sites. We proposed a mechanistic model for the competitive regulation by the two TFs on a defined subset of sporulation genes. We show that this model accounts for the temporal control of the "middle" sporulation genes and suggest a similar regulatory arrangement can be found in developmental programs in higher organisms.

Algorithms↗
PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗
PubMed1986

A model to assess survival mechanisms of parasites in a genetically defined host system.

We examined a host system (peromyscus maniculatus) in which a single autosomal gene controls susceptibility or resistance to infection by the cestode parasite Hymenolepis citelli. Parasite deaths of both primary and secondary (challenge) infections were examined, using a probabilistic model, to see if deaths were random and uncorrelated within each genotype. Within susceptible hosts, post-patent survival of primary worms was not random and heterogeneity was due to among-host effects rather than parasite-age effects, suggesting a second genetic or immunological process. Secondary infections in susceptible hosts appear to be lost randomly, independent of primary infection age or burden. The loss rate is similar to that for primary worms. The lack of correlation or alteration in (susceptible) host response to primary and secondary infections suggests that the latter are eliminated by a process that differs from that acting on primary worms. In resistant hosts, parasite survival rates suggest that the immunological process elicited by the primary infection also acts to eliminate the secondary infection more rapidly. Suggestions are made for improving experimental methods when dealing with mixed genotype populations. Experiments should permit direct estimation of genotype frequency and of parasite death rates within each genotype. This separation of host type is particularly important when studying correlation of successive worm burdens since any host or treatment mixture (genotype, age, sex, size or infection dose) may induce correlations that could be mistakenly interpreted.

Age Factors↗
PubMed1995

Bacterial inoculum density and probability of para-nitrophenol biodegradability test response.

This study has been carried out to establish a model linking probability of positive response in para-nitrophenol biodegradability test to controlled variables of the test (suspended solids, SS; total bacteria, AODC; cultivable bacteria, CFU; specific biodegraders, MPN). Series of dilution of 11 raw inocula (6 activated sludges, 5 river waters) were tested. They reveal very dispersed values of biomass measured as SS, AODC, and CFU and quite comparable values of specific biodegraders for each category of inoculum (river or sludge). The proposed model fits well the empirical distribution of the experimental frequency of positive results versus inoculum density for each controlled variable. The constants k of the model, representing the fraction of biodegraders for each inoculum, were tested by the likelihood ratio test and were proven to be different from one another according to the biomass descriptor and the origin of the inoculum. The probabilistic model, in the case of para-nitrophenol biodegradation, indicates that standardized official tests (closed bottle, AFNOR, Sturm, and MITI I) are seldom optimal under those conditions. It allows the determination of which inoculum concentration can lead to a high probability (e.g., 99.9%) of observing paranitrophenol biodegradation by raw inocula.

Bacteria↗
PubMed1999

Decision analysis on alternative treatment strategies for favorable-prognosis, early-stage Hodgkin's disease.

PURPOSE: To compare the therapeutic outcomes of various treatment strategies in early-stage, favorable-prognosis Hodgkin's disease (HD) using methods of decision analysis. METHODS: We constructed a decision-analytic model to determine the life expectancy and quality-adjusted life expectancy for a hypothetical cohort of clinically or pathologically staged 25-year-old patients with early-stage, favorable-prognosis HD treated with varying degrees of initial therapy. Markov models were used to simulate the lifetime clinical course of patients, and baseline probability estimates were derived from published study results. RESULTS: Among patients with pathologic stage (PS) I to II, mantle and para-aortic (MPA) radiotherapy was favored over combined-modality therapy (CMT), mantle radiotherapy, and chemotherapy by 1.18, 1.33, and 1.55 years, respectively. For patients with clinical stage (CS) I to II, the treatment options of MPA-splenic radiotherapy, CMT, and chemotherapy yielded similar survival outcomes. Sensitivity analysis showed that the decision between CMT and MPA-splenic radiotherapy was highly influenced by the precise values of the estimates of treatment efficacy and long-term morbidity, the quality-of-life value assigned to the postsplenic irradiation state, and the time discount value used in the model. Probabilistic sensitivity analysis demonstrated that even if future studies doubled the precision of the estimates of the treatment-related variables, it would be impossible to demonstrate the superiority of one treatment over the other. CONCLUSION: Our model predicted that on average, MPA radiotherapy was clearly the preferred treatment for PS I to II patients. For CS I to II patients the treatment decision is a toss-up between MPA-splenic radiotherapy and CMT, emphasizing the importance of patient preference exploration and shared decision making between patient and physician when choosing between treatments.

Child↗
PubMed1992

Model parameters estimation when the evoked potential recordings are affected by a random scale factor.

In many situations an important source of the average evoked potentials (EPs) variability is a random scale factor affecting each recording. As a result, the outcome of any EP detection method may be greatly affected. However, using an appropriate probabilistic model these scale factor can be estimated, and the performance of any available detection index improved by data rescaling. In this paper the Maximum Likelihood Estimators of the waveform of the response and the scale factor affecting both background noise and this waveform are obtained. Also, an iterative algorithm for model parameters estimation is presented and its convergence is examined in a simulation study. The Linear Discriminant function is computed using simulated test data in both situations, before and after rescaling of recordings. The performance of these statistics is evaluated by mean of ROC curves.

Algorithms↗
PubMed2006

A dynamic artificial clam (Corbicula fluminea) allows parsimony on-line measurement of waterborne metals.

We introduce a novel on-line biomonitoring system based on a valvometric conversion technique for clam Corbicula fluminea, allowing for rapid, continuous, and ecological relevant water quality control. Our model builds upon the basic principles of biological early warning system model in two ways. We first adopted a risk-based methodology to build a dynamic artificial clam for simulating how the bivalve closure rhythm in response to waterborne copper (Cu) and cadmium (Cd). Secondly, we integrated a probabilistic model associated with the time-varying dose-response relationships of valve closing behavior into the mechanisms of a dynamic artificial clam, allowing estimation of the time-varying waterborne Cu/Cd concentrations for on-line providing the outcomes of the toxicity detection technique. Measurements with Cu/Cd were performed and the calculated EC50 values were compared with published data for the valve movement test with C. fluminea. This proposed dynamic artificial clam provides a better quantitative understanding of on-line biomonitoring measurements of waterborne metals and may foster applications in clam farm management strategy and ecotoxicological risk assessment.

Animals↗
PubMed1996

Modelling the power spectra of natural images: statistics and information.

Power spectra of an extensive set of natural images were analysed. Both the total power in a spectrum (corresponding to image contrast) and its dependence on spatial frequency vary considerably between images, and also within images when considered as functions of orientation. A series of probabilistic models for power spectra enabled calculating the information obtained from prior knowledge of parameters describing spectra. Most information is gained from contrast, 1/f2 spatial frequency behaviour, and contrast as a function of orientation. Variations in spatial frequency behaviour are relatively unimportant. For oriented contrast, a bandwidth of 10-30 deg is sufficient to obtain most information.

Contrast Sensitivity↗
PubMedDate not supplied

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Artificial Intelligence↗
PubMed2006

Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints.

BACKGROUND: We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm. RESULTS: We use probabilistic models (pair stochastic context-free grammars, pairSCFGs) as a unifying framework for scoring pairwise alignment and folding. A constrained version of the pairSCFG structural alignment algorithm was developed which assumes knowledge of a few confidently aligned positions (pins). These pins are selected based on the posterior probabilities of a probabilistic pairwise sequence alignment. CONCLUSION: Pairwise RNA structural alignment improves on structure prediction accuracy relative to single sequence folding. Constraining on alignment is a straightforward method of reducing the runtime and memory requirements of the algorithm. Five practical implementations of the pairwise Sankoff algorithm - this work (Consan), David Mathews' Dynalign, Ian Holmes' Stemloc, Ivo Hofacker's PMcomp, and Jan Gorodkin's FOLDALIGN - have comparable overall performance with different strengths and weaknesses.

Algorithms↗
PubMed2004

Potential targets for anti-SARS drugs in the structural proteins from SARS related coronavirus.

This is a further study on the severe acute respiratory syndrome (SARS) using the probabilistic models. The purpose was to define the potential targets for anti-SARS drugs in the structural proteins from human SARS related coronavirus (SARS-CoV) while knowing little about the functional sites and possible mutations in these proteins. From a probabilistic viewpoint, we can theoretically select the amino acid pairs as potential candidates for anti-SARS drugs. These candidates have a greater chance of colliding with anti-SARS drugs, are more likely to link with the protein functions and are less vulnerable to mutations.

Antiviral Agents↗
PubMed1999

Predicting risk of decompression sickness in humans from outcomes in sheep.

In animals, the response to decompression scales as a power of species body mass. Consequently, decompression sickness (DCS) risk in humans should be well predicted from an animal model with a body mass comparable to humans. No-stop decompression outcomes in compressed air and nitrogen-oxygen dives with sheep (n = 394 dives, 14.5% DCS) and humans (n = 463 dives, 4.5% DCS) were used with linear-exponential, probabilistic modeling to test this hypothesis. Scaling the response parameters of this model between species (without accounting for body mass), while estimating tissue-compartment kinetic parameters from combined human and sheep data, predicts combined risk better, based on log likelihood, than do separate sheep and human models, a combined model without scaling, and a kinetic-scaled model. These findings provide a practical tool for estimating DCS risk in humans from outcomes in sheep, especially in decompression profiles too risky to test with humans. This model supports the hypothesis that species of similar body mass have similar DCS risk.

Algorithms↗