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Results for “Uncertainty Quantification”

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At least 19 recordsLinked to original sources

PubMed1998

Undocumented residents in the United States in 1990: issues of uncertainty in quantification.

"Uncertainties are abundant about the measurement of net undocumented migration [to the United States] and change over the past two decades. This analysis presents possible upper and lower boundaries on components for estimating legal migration in 1980-1989 and on the foreign-born population in 1990. Positing ranges for net undocumented immigration, between 2 million and 4 million undocumented residents may have been counted in the 1990 census. The total number of undocumented residents may have been as high as 6 million."

Americas↗

PubMed1999

Uncertainty quantification in the health consequences of the boiling liquid expanding vapour explosion phenomenon.

A methodology for estimating the risk owing to the phenomenon of boiling liquid expanding vapour explosion (BLEVE) in the presence of uncertainties both in the model and in the parameters of the models is presented. BLEVE takes place when a tank containing liquefied petroleum gas (LPG) is exposed to fire and fails catastrophically. Two models have been used in the estimation of the intensity of thermal radiation from the resulting fireball, namely the solid-flame model assuming an emission power independent of the combustion mass and the point-source model that estimates the emissive power as a function of the combustion mass. Three measures of the BLEVE consequences, the intensity of thermal radiation, the dose of thermal radiation and the probability of loss of life as a result of the exposure to the thermal radiation and as a function of the distance from the center of the tank have been considered. Uncertainties in the exact values of the parameters of the models have been quantified and the resulting uncertainties in the three consequence measures have been assessed. A sensitivity analysis on the relative contribution of the uncertainty in each of the input variables to the uncertainties of the consequence measures has been performed. One conclusion is that the uncertainties in the probability of loss of life are mainly due to the uncertainties in the model of the physical phenomenon rather than to the uncertainties of the dose-response model.

Accidents, Occupational↗

PubMed2026

A Bayesian framework for multivariate differential analysis.

Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the main illustration of the present paper. The state-of-the-art limma approach uses a hierarchical formulation with moderated-variance estimators for each analyte directly injected into the t-statistic. While standard hypothesis testing strategies are recognised for their low computational cost, allowing for quick extraction of the most differential among thousands of elements, they generally overlook key aspects such as handling missing values, inter-element correlations, and uncertainty quantification. The present paper proposes a fully Bayesian framework for differential analysis, leveraging a conjugate hierarchical formulation for both the mean and the variance. Inference is performed by computing the posterior distribution of compared experimental conditions and sampling from the distribution of differences. This approach provides well-calibrated uncertainty quantification at a similar computational cost as hypothesis testing by leveraging closed-form equations. Furthermore, a natural extension enables multivariate differential analysis that accounts for possible inter-element correlations. We also demonstrate that, in this Bayesian treatment, missing at random data should generally be ignored in univariate settings, and further derive a tailored approximation that handles multiple imputation for the multivariate setting. We argue that probabilistic statements in terms of effect size and associated uncertainty are better suited to practical decision-making. Therefore, we finally propose simple and intuitive inference criteria, such as the overlap coefficient, which express group similarity as a probability rather than traditional, and often misleading, p-values. The performance of this approach is evaluated through an extensive empirical study using both synthetic and controlled real-world proteomics datasets. Overall, we believe that this Bayesian framework for (multivariate) differential analysis provides a valuable and intuitive counterpart to standard methods at a comparable computational cost.

Bayes Theorem↗

PubMed2026

Estimating protein isoform abundances with [Formula: see text].

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce [Formula: see text] (Protein isoform Abundance Quantification), a Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. [Formula: see text] offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that [Formula: see text] consistently outperforms competing methods in detecting differentially abundant protein isoforms and estimating their abundances. We use [Formula: see text] to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long-held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that [Formula: see text] can identify significant variations in isoform abundance levels not previously possible.

Protein Isoforms↗

PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗

PubMed2026

Improving the reliability of polygenic risk score-based prediction for cardiovascular and renal complications across ancestries in type 2 diabetes using Mondrian Cross-Conformal Prediction.

Polygenic risk scores (PRS) developed in European populations often show reduced predictive performance in non-European populations, limiting their clinical utility. This lack of transferability across ancestries remains a major challenge in genomic medicine and raises concerns about health equity. We aimed to evaluate whether uncertainty-aware prediction, implemented through Mondrian Cross-Conformal Prediction, improves the performance and reliability of polygenic risk score-based predictions across ancestries for nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes in a multi-ethnic cohort. We leveraged Mondrian Cross-Conformal Prediction (MCCP), an uncertainty quantification framework, combined with logistic regression applied to a multi-polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes. Two training frameworks were evaluated: one using 4,098 individuals with type 2 diabetes of European ancestry from the ADVANCE trial for training and 17,574 White British, 1,145 South Asian, and 749 African UK Biobank participants for testing; and another using the 17,574 White British UK Biobank participants for training and the South Asian and African participants for testing. Logistic regression provided robust baseline performance across populations. On top of this baseline, MCCP did not improve performance but added capabilities absent from probability-based stratification: for each individual, it issued a prediction together with an explicit confidence and credibility level; it allowed a tolerated error level to be set in advance and delivered prediction sets respecting it in the majority of settings; and it flagged individuals for whom no reliable prediction could be made. Applying MCCP to PRS-based prediction thus enables uncertainty-aware risk stratification and improves the reliability of risk prediction across ancestries, providing a more equitable framework for clinical use.

Female↗

PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗

PubMed2025

Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.

The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. Here, to address these challenges, we introduce SINGER (sampling and inferring of genealogies with recombination), a method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for hundreds of whole-genome sequences. Through extensive simulations, we demonstrate SINGER's enhanced accuracy and robustness to model misspecification compared to existing methods. We demonstrate the utility of SINGER by applying it to individuals of British and African descent within the 1000 Genomes Project, identifying signals of population differentiation, archaic introgression and strong support for ancient polymorphism in the human leukocyte antigen region shared across primates.

Humans↗

PubMed2026

TPMM: three-component posterior mixture model enables robust inverton detection in low-depth metagenomes and suggests potential viral invertons.

SUMMARY: Bacterial phase variation enables reversible, locus-specific phenotypic switching, often driven by DNA inversion (invertons). To identify these events, researchers commonly rely on sequencing reads that provide orientation-specific support. Metagenomic sequencing, which captures total genetic material independent of cultivation, offers a powerful platform for the comprehensive study of invertons. However, computational inverton calling from metagenomic data is difficult at low sequencing depth: hard read-support cutoffs can miss true events, while sequence-only predictors lack read-backed interpretability and uncertainty quantification. To address this, we present TPMM, a three-component posterior mixture model for inverton calling in metagenomic data. TPMM explicitly incorporates sequencing depth to formulate inverton detection as a probabilistic mixture problem. Starting from candidates flanked by inverted repeats, the model classifies the candidates into noise, low-probability, or high-probability inversion signals using read evidence. Finally, TPMM assigns posterior probabilities as soft labels and applies cumulative Bayesian False Discovery Rate control to robustly identify true invertons. On two real gut metagenomic datasets, TPMM agrees well with PhaseFinder at high depth but recovers substantially more invertons under systematic downsampling, demonstrating superior performance in sparse-data regimes. We further examine potential reversible inversion elements in viral genomes and provide supporting analyses, suggesting a broader scope for inversion-mediated regulation. AVAILABILITY: The source code of TPMM is available via: https://github.com/KennyxxD/TPMM.

Metagenomics↗

PubMed2006

Rapid quantification of DNA methylation through dNMP analysis following bisulfite-PCR.

We report a novel method for rapid quantification of the degree of DNA methylation of a specific gene. Our method combined bisulfite-mediated PCR and quantification of deoxyribonucleoside monophosphate (dNMP) contents in the PCR product through capillary electrophoresis. A specific bisulfite-PCR product was enzymatically hydrolyzed to dNMP monomers which were quantitatively analyzed through subsequent capillary electrophoresis. PCR following bisulfite treatment converts unmethylated cytosines to thymines while leaving methyl-cytosines unchanged. Then the ratio of cytosine to thymine determined by capillary electrophoresis represents the ratio of methyl-cytosine to cytosine in genomic locus of interest. Pure oligonucleotides with known sequences were processed in parallel as standards for normalization of dNMP peaks in capillary electrophoresis. Sources of quantification uncertainty such as carryovers of dNTPs or primers and incomplete hydrolysis were examined and ruled out. When the method was applied to samples with known methylation levels (by bisulfite-mediated sequencing) as a validation, deviations were within +/-5%. After bisulfite-PCR, the analytical procedure can be completed within 1.5 h.

Cytosine↗

PubMed2026

Calibrated Prediction Intervals for Polygenic Scores: Updated Comparisons, Contextual Calibration, and Data Normalization.

Calibrated prediction intervals for polygenic scores (PGS) are essential for communicating individual-level uncertainty in genomic medicine. We present updated comparisons of two methods for constructing such intervals: CalPred, a parametric approach, and PredInterval, a non-parametric approach. Our results show that both methods can achieve calibrated coverage, although CalPred additionally requires a sufficiently large calibration set. The two methods also exhibit complementary trade-offs with respect to dataset size and risk identification. We further show that contextual calibration, as introduced in Hou et al. and followed in Shi et al., is most naturally achieved through appropriate phenotype normalization and data preprocessing. Apparent miscalibration can arise from inadequate normalization or from providing contextual information to some methods but not others. In UK Biobank, standard GWAS phenotype normalization procedures are sufficient to achieve contextual calibration for traits analyzed. In the extreme simulations of Hou et al. and Shi et al., supplying contextual covariates to PredInterval restores contextual calibration without normalization, and appropriate normalization can achieve contextual calibration without supplying covariates, while also substantially improving upstream tasks including association power and PGS accuracy. Together, these results underscore the central role of phenotype normalization and data preprocessing in GWAS analyses, including reliable uncertainty quantification for PGS.

Journal Article↗

PubMed2006

Sources of uncertainty in the quantification of genetically modified oilseed rape contamination in seed lots.

Testing of seed and grain lots is essential in the enforcement of GM labelling legislation and needs reliable procedures for which associated errors have been identified and minimised. In this paper we consider the testing of oilseed rape seed lots obtained from the harvest of a non-GM crop known to be contaminated by volunteer plants from a GM herbicide tolerant variety. The objective was to identify and quantify the error associated with the testing of these lots from the initial sampling to completion of the real-time PCR assay with which the level of GM contamination was quantified. The results showed that, under the controlled conditions of a single laboratory, the error associated with the real-time PCR assay to be negligible in comparison with sampling error, which was exacerbated by heterogeneity in the distribution of GM seeds, most notably at a small scale, i.e. 25 cm3. Sampling error was reduced by one to two thirds on the application of appropriate homogenisation procedures.

Base Sequence↗

PubMed2004

Quantification of modelling uncertainties in a large ensemble of climate change simulations.

Comprehensive global climate models are the only tools that account for the complex set of processes which will determine future climate change at both a global and regional level. Planners are typically faced with a wide range of predicted changes from different models of unknown relative quality, owing to large but unquantified uncertainties in the modelling process. Here we report a systematic attempt to determine the range of climate changes consistent with these uncertainties, based on a 53-member ensemble of model versions constructed by varying model parameters. We estimate a probability density function for the sensitivity of climate to a doubling of atmospheric carbon dioxide levels, and obtain a 5-95 per cent probability range of 2.4-5.4 degrees C. Our probability density function is constrained by objective estimates of the relative reliability of different model versions, the choice of model parameters that are varied and their uncertainty ranges, specified on the basis of expert advice. Our ensemble produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulation.

Journal Article↗

PubMed1996

Quantification of the uncertainties in resultant joint moments computed in a dynamic activity.

Resultant joint moments are an important variable with which to examine human movement, but the uncertainty with which resultant joint moments are calculated is often ignored. This paper presents a procedure for examining the uncertainty with which resultant joint moments are calculated. The uncertainty was calculated by changing the parameters and variables required to compute the resultant joint moments, by amounts relating to their estimated uncertainties, and then quantifying the resulting change in the resultant joint moments. The procedure was applied to the elbow joint during loaded elbow flexion executed at maximum volitional speed. For this activity, the estimated moments were most sensitive to uncertainties in the derivatives of the position data. A number of other sources of error and uncertainty were identified which warrant further investigation. The protocols outlined in this study are applicable to other activities.

Biomechanical Phenomena↗

PubMed2001

Uncertainty estimation on the quantification of major milk proteins by liquid chromatography.

A detailed uncertainty budget is evaluated for the quantification of major milk proteins separated by reversed phase ion-pair high performance liquid chromatography (RP-IP HPLC). Recommendations from the International Organisation for Standardisation, Guide to the Expression of Uncertainty and the EURACHEM/CITAC Guide "Quantifying Uncertainty in Analytical Measurement" were followed resulting in the expression of combined uncertainties as an expanded uncertainty. The authors have identified the lack of a detailed description on all possible sources of uncertainty on chromatographic milk protein separations. Up to now, uncertainty has only been expressed as a random chromatographic variability, being exclusively measured by regression equations. On the contrary, the establishment of a full uncertainty budget permits a complete identification of all sources of uncertainty associated with the measurement. Thus, a better comprehension of the method is obtained. Having identified all sources of uncertainty, to the best of our knowledge, they are minimised (whenever possible) and combined. The importance of this identification is crucial, in particular, concerning previously neglected sources, in this example the combination of the interpolated injected mass of protein and its purity, having a dominant influence on the total combined uncertainty. The quality of the measurement (measured by its associated uncertainty statement, which should be based on a full uncertainty budget evaluation) is demonstrated. Moreover, due to the high precision of the proposed method, the total protein content, in addition to the group protein composition (i.e. total casein and whey protein content) is calculated with high reliability, which is an extremely useful factor regarding the establishment of milk provenance/adulteration. Combined relative standard uncertainties ranging from 3.3 up to 12.5% for individual protein concentration were found, whereas for total protein content, an overall combined relative standard uncertainty of 2.7% (liquid) and 3.3% (powdered samples) was achieved.

Animals↗

PubMed2003

Uncertainty in toxicological risk assessment for non-carcinogenic health effects.

Uncertainty in risk assessment results from the lack of knowledge on toxicity to the target population for a substance. Currently used deterministic risk assessment methods yield human limit values or margins of safety (MOS) without quantitative measurements of uncertainty. Qualitative and quantitative uncertainty analysis would enable risk managers to better judge the consequences of different management options. This article discusses sources of uncertainty and possibilities for quantification of uncertainty associated with different steps in the risk assessment of non-carcinogenic health effects. Knowledge gaps causing uncertainty in risk assessment are overcome by extrapolation. Distribution functions for extrapolation factors are based on empirical data and provide information about the extent of uncertainty introduced by these factors. Whereas deterministic methods can account only qualitatively for uncertainty of the resulting human limit value, probabilistic risk assessment methods are able to quantify several aspects of uncertainty. However, there is only limited experience with these methods in practice. Their acceptance and future application will depend on the establishment of evidence based distribution functions, flexibility and practicability of the methods, and the unambiguity of the results.

Animals↗

PubMed2002

Quantification of variability and uncertainty in air pollutant emission inventories: method and case study for utility NOx emissions.

The quality of stationary source emission factors is typically described using data quality ratings, which provide no quantification of the precision of the emission factor for an average source, nor of the variability from one source to another within a category. Variability refers to actual differences caused by differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions. A general methodology for the quantification of variability and uncertainty in emission factors, activity factors, and emission inventories (EIs) is described, featuring the use of bootstrap simulation and related techniques. The methodology is demonstrated via a case study for a selected example of NOx emissions from coal-fired power plants. A prototype software tool was developed to implement the methodology. The range of interunit variability in selected activity and emission factors was shown to be as much as a factor of 4, and the range of uncertainty in mean emissions is shown to depend on the interunit variability and sample size. The uncertainty in the total inventory of -16 to +19% was attributed primarily to one technology group, suggesting priorities for collecting data and improving the inventory. The implications for decision-making are discussed.

Air Pollutants↗

PubMed2004

Quantification of variability and uncertainty for censored data sets and application to air toxic emission factors.

Many environmental data sets, such as for air toxic emission factors, contain several values reported only as below detection limit. Such data sets are referred to as "censored." Typical approaches to dealing with the censored data sets include replacing censored values with arbitrary values of zero, one-half of the detection limit, or the detection limit. Here, an approach to quantification of the variability and uncertainty of censored data sets is demonstrated. Empirical bootstrap simulation is used to simulate censored bootstrap samples from the original data. Maximum likelihood estimation (MLE) is used to fit parametric probability distributions to each bootstrap sample, thereby specifying alternative estimates of the unknown population distribution of the censored data sets. Sampling distributions for uncertainty in statistics such as the mean, median, and percentile are calculated. The robustness of the method was tested by application to different degrees of censoring, sample sizes, coefficients of variation, and numbers of detection limits. Lognormal, gamma, and Weibull distributions were evaluated. The reliability of using this method to estimate the mean is evaluated by averaging the best estimated means of 20 cases for small sample size of 20. The confidence intervals for distribution percentiles estimated with bootstrap/MLE method compared favorably to results obtained with the nonparametric Kaplan-Meier method. The bootstrap/MLE method is illustrated via an application to an empirical air toxic emission factor data set.

Air↗