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PubMed2026

GiantHost: a domain-adaptive and uncertainty-aware framework for giant virus host prediction.

MOTIVATION: Nucleocytoplasmic large DNA viruses (NCLDVs) play crucial roles in global ecosystems. Although metagenomics has vastly accelerated the discovery of novel NCLDVs, predicting their hosts from fragmented contigs remains a critical bottleneck, with no dedicated end-to-end computational tools currently available. Addressing this gap requires overcoming three fundamental challenges: the extreme scarcity of labeled reference genomes, the severe domain shift between laboratory isolates and diverse environmental metagenomes, and the inability of traditional deterministic models to quantify prediction uncertainty-a crucial requirement for reliable ecological profiling where novel, divergent viruses are prevalent. RESULTS: We present GiantHost, the first NCLDV host prediction tool with domain adaptation and uncertainlty awareness. GiantHost employs a dual-tower neural network to integrate dense genome traits and sparse GVOG profiles, allowing better integration of heterogeneous features. To overcome label scarcity and domain shift, we leverage 1400 environmental viral genomes (GVMAGs) via semi-supervised multi-task learning and Domain Adversarial Neural Networks (DANN), effectively bridging the distributional gap between RefSeq and environmental data. Additionally, GiantHost incorporates Conformal Prediction (CP) to output statistically guaranteed prediction sets rather than overconfident single labels. Evaluated under rigorous genome-level cross-validation, GiantHost demonstrates robust predictive power. Applied to the Tara Ocean dataset, GiantHost successfully captured the vertical stratification of NCLDV hosts-revealing a depth-dependent decline of phytoplankton-infecting viruses and a relative enrichment of Amoebozoa-infecting viruses in the mesopelagic zone. AVAILABILITY: The source code of GiantHost is available via: https://github.com/FuchuanQu/GiantHost.

Giant Viruses↗

PubMed2006

Pvclust: an R package for assessing the uncertainty in hierarchical clustering.

SUMMARY: Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (BP) value. Multiscale bootstrap resampling is used for the calculation of AU p-value, which has superiority in bias over BP value calculated by the ordinary bootstrap resampling. In addition the computation time can be enormously decreased with parallel computing option.

Algorithms↗

PubMed2006

The influence of phylogenetic uncertainty on the detection of positive Darwinian selection.

The power of maximum likelihood tests of positive selection on protein-coding genes depends heavily on detecting and accounting for potential biases in the studied data set. Although the influence of transition:transversion and codon biases have been investigated in detail, little is known about how inaccuracy in the phylogeny used during the calculations affects the performance of these tests. In this study, 3 empirical data sets are analyzed using sets of simulated topologies corresponding to low, intermediate, and high levels of phylogenetic uncertainty. The detection of positive selection was largely unaffected by errors in the underlying phylogeny. However, the number of sites identified as being under positive selection tended to be overestimated.

Animals↗

PubMedDate not supplied

Uncertainty in health care environments: myth or reality?

Health care leaders and analysts typically describe the health care environment as dynamic, complex, and highly uncertain. This study conceptualizes environmental uncertainty as an individual perception that blends subjective and objective realities derived from the complexity and dynamism of the organizational task environment. Exploratory judgments of the complexity and dynamism of the environment of health care organizations are included.

Attitude of Health Personnel↗

PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗

PubMed2006

Assessment of clinical reasoning in the context of uncertainty: the effect of variability within the reference panel.

The Script Concordance Test (SCT) assesses reasoning in the context of uncertainty. Because there is no single correct answer, scoring is based on the comparison of answers provided by examinees with those provided by members of a reference panel made up of experienced practitioners. The study aimed to assess the discriminatory power of the SCT based on the variability of the reference panel's answers. Items from a bank covering different family medicine domains were classified into 3 groups according to the degree of variability of answers provided by a pool of experienced doctors. A variability index (mean squared error) was used to select items in the low, moderate and high variability categories. A 102-item test (Cronbach's alpha 0.70), made up of 3 subtests of each category, was administered to 3 contrasting groups in family medicine: 157 clerkship students, 30 residents and 30 practising doctors. anova and effect size (ES) were used to quantify and test the discrimination power of the 3 subtests. The high variability subtest showed high effect size for discrimination between extreme groups (ES = 1.5; F = 16.3, P < 0.001), whereas the moderate variability subtest showed less effect size (ES = 0.56; F = 57, P = 0.041). The low variability subtest did not discriminate significantly (ES = 0.31; F = 2.9, P = 0.06). Variability of answers within the reference panel is a key component of the discriminatory power of the SCT. In accordance with theory, the presence of variability ensures discrimination between levels of clinical experience. These results imply important considerations for the construction of efficient SCTs.

Analysis of Variance↗

PubMed2006

Cardiac pain or panic disorder? Managing uncertainty in the emergency department.

Emergency department admissions for chest pain might be related to a psychological problem, such as panic. Panic disorder adversely affects the person's lifestyle, general health, and psychological well-being. Panic disorder is difficult to recognize, so nursing assessment and management of these clients is important. This paper presents research findings from a New Zealand study that explored emergency nurses' differentiation of non-cardiac chest pain from panic disorder and raised significant issues in the nursing assessment and management of such clients. The data were gathered from focus group interviews and were analyzed thematically. Three themes, prioritizing time, managing uncertainty and ambiguity, and the life-threatening lens, were identified. The findings confirm that a panic disorder is not always diagnosed when biomedical assessment is used in isolation from a psychosocial assessment. Emergency nurses are pivotal in reversing the cycle of repeat presenters with non-cardiac chest pain. Recommendations for assessing and managing this complex condition are presented.

Adult↗

PubMedDate not supplied

Identifying the potential loss of monitoring wells using an uncertainty analysis.

From the mid-1940s through the 1980s, large volumes of waste water were discharged at the Hanford Site in southeastern Washington State, causing a large-scale rise (>20 m) in the water table. When waste water discharges ceased in 1988, ground water mounds began to dissipate. This caused a large number of wells to go dry and has made it difficult to monitor contaminant plume migration. To identify monitoring wells that will need replacement, a methodology has been developed using a first-order uncertainty analysis with UCODE, a nonlinear parameter estimation code. Using a three-dimensional, finite-element ground water flow code, key parameters were identified by calibrating to historical hydraulic head data. Results from the calibration period were then used to check model predictions by comparing monitoring wells' wet/dry status with field data. This status was analyzed using a methodology that incorporated the 0.3 cumulative probability derived from the confidence and prediction intervals. For comparison, a nonphysically based trend model was also used as a predictor of wells' wet/dry status. Although the numerical model outperformed the trend model, for both models, the central value of the intervals was a better predictor of a wet well status. The prediction interval, however, was more successful at identifying dry wells. Predictions made through the year 2048 indicated that 46% of the wells in the monitoring well network are likely to go dry in areas near the river and where the ground water mound is dissipating.

Environmental Monitoring↗

PubMed2005

Neural systems responding to degrees of uncertainty in human decision-making.

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.

Adult↗

PubMed1996

Equipoise as a means of managing uncertainty: personal, communal and proxy.

Equipoise is advocated as a means of achieving high scientific and ethical standards in randomised trials. As used in the context of research the word describes a state of uncertainty characterised by the belief that in a trial no arm is known to offer greater harm or benefit than any other arm. Clinicians who lack personal equipoise are advised to accept clinical or communal equipoise, based on current unresolved disagreement among the medical profession. Equipoise is mainly discussed in the literature as an issue for senior doctors and research directors. Limitations of professional equipoise are reviewed, and data on the neglected topic of patients' equipoise are reported using the example of breast cancer trials. In theory, a patient who gives informed and voluntary consent to enter a randomised trial has achieved the equilibrium of equipoise. In practice, equipoise among patients ranges from personal to proxy acceptance.

Adult↗

PubMed2004

Relative uncertainty learning theory: an essay.

The aim of this manuscript is to present a detailed analysis of the algebraic and geometric properties of relative uncertainty theory (RUT) applied to neural networks learning. Through the algebraic analysis of the original learning criterion, it is shown that RUT gives rise to principal-subspace-analysis-type learning equations. Through an algebraic-geometric analysis, the behavior of such matrix-type learning equations is illustrated, with particular emphasis to the existence of certain invariant manifolds.

Humans↗

PubMed2001

Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies.

As many more clinical trials collect economic information within their study design, so health economics analysts are increasingly working with patient-level data on both costs and effects. In this paper, we review recent advances in the use of statistical methods for economic analysis of information collected alongside clinical trials. In particular, we focus on the handling and presentation of uncertainty, including the importance of estimation rather than hypothesis testing, the use of the net-benefit statistic, and the presentation of cost-effectiveness acceptability curves. We also discuss the appropriate sample size calculations for cost-effectiveness analysis at the design stage of a study. Finally, we outline some of the challenges for future research in this area-particularly in relation to the appropriate use of Bayesian methods and methods for analyzing costs that are typically skewed and often incomplete.

Bayes Theorem↗

PubMed2006

Time, uncertainty, and individual differences in decisions to cooperate in resource dilemmas.

Many environmental problems are resource dilemmas that contain two components-social and temporal-that encourage individuals not to act in the best interest of the group. Most research has focused on the social component. The authors examined the importance of the temporal component in two resource dilemma tasks. Participants (N = 112) reported their willingness to limit resource consumption in response to hypothetical dilemmas and forfeited extra credit points in response to a real dilemma. Cooperation rates were higher when the temporal dilemma was diminished, when uncertainty about environmental impacts was low, and for women. Individuals high in both proenvironmentalism and consideration of future consequences sustained high levels of cooperation even in the face of strong temporal dilemmas.

Adolescent↗

PubMed2006

Confidence and uncertainty long after initial treatment for early prostate cancer: survivors' views of cancer control and the treatment decisions they made.

PURPOSE: The many years most men diagnosed with early prostate cancer live after diagnosis allow evolving assessments of their cancer control and their treatment choices, but little is known of these outcomes or the factors that influence them. PATIENTS AND METHODS: We surveyed an established, prospective cohort that had initiated treatment for early prostate cancer 4 to 8 years previously. We assessed perceived cancer control, quality of treatment decisions, and other domains of quality of life, along with treatment-related urinary, bowel, and sexual dysfunction. RESULTS: Most men reported high confidence in cancer control and their treatment decisions, but many reported misgivings about one or both. The diagnostic, treatment, and quality-of-life factors associated with these two outcomes were distinct. Perceived cancer control was lower among those with adverse medical factors: higher pretreatment Gleason scores, subsequent rises in prostate-specific antigen (PSA), and secondary androgen ablation therapy. Confidence in treatment decisions was unrelated to these factors and was higher in men who opted for radical prostatectomy or brachytherapy, reported close attention to current PSA, had high masculine self-esteem and little distress from sexual dysfunction, and were married. CONCLUSION: Although perceptions of cancer control and the quality of their treatment decisions are linked, men can distinguish between these two outcomes. They incorporate objective indicators of high risk and adverse outcomes when assessing their cancer control; confidence in treatment decisions represents a more complex psychosocial adjustment to the persistent uncertainty that originates with their diagnosis.

Aged↗

PubMed1994

A toxicological basis to derive a generic interspecies uncertainty factor.

A new method is proposed to derive the size of the interspecies uncertainty factor (UF) that is toxicologically and statistically based. The method involves quantifying interspecies variation in susceptibility to numerous toxic substances via the use of binary interspecies comparisons that are converted to a 95% UF. This interspecies UF represents an estimate of the upper 95% of the population of 95% prediction intervals (PI) for binary interspecies comparisons within four categories of phylogenetic relatedness (species within genus, genera within family, families within order, orders within class). The 95% interspecies UFs range from a low of 10 for species within genus up to 65 for orders within class. Most mammalian toxicology studies involving mice, rats, cats, dogs, gerbils, and rabbits are orders-within-class categories for human risk assessment and would be provided a 65-fold UF. Larger or smaller interspecies UF values could be selected on the level of protection desired.

Animals↗

PubMed2006

Uncertainty analysis of single-concentration exposure data for risk assessment--introducing the species effect distribution approach.

In recent years, the inclusion of uncertainty analysis in risk assessment has been much debated. One pertinent issue is the translation of the effects observed with a limited number of test species to a general protection level for most or all species present in the environment. In a number of cases, toxicity data may consist of data from tests employing only a control and one treatment. Given that more species (or processes) have been tested with the same treatment, the treatment can be considered as fixed, and the effect level of the individual species (or processes) can be considered as variable. The distribution of effects can be viewed as a species effect distribution for that treatment. The distribution will represent all organisms and may be used to predict the maximum impact on any fraction of all organisms (e.g., 95% of all species). Hence, it is possible to predict the maximum effect level, with a selected certainty, for a given fraction of all species.

Animals↗

PubMed2006

Determination of protection zones for Dutch groundwater wells against virus contamination--uncertainty and sensitivity analysis.

Protection zones of shallow unconfined aquifers in The Netherlands were calculated that allow protection against virus contamination to the level that the infection risk of 10(-4) per person per year is not exceeded with a 95% certainty. An uncertainty and a sensitivity analysis of the calculated protection zones were included. It was concluded that protection zones of 1 to 2 years travel time (206-418 m) are needed (6 to 12 times the currently applied travel time of 60 days). This will lead to enlargement of protection zones, encompassing 110 unconfined groundwater well systems that produce 3 x 10(8) m3 y(-1) of drinking water (38% of total Dutch production from groundwater). A smaller protection zone is possible if it can be shown that an aquifer has properties that lead to greater reduction of virus contamination, like more attachment. Deeper aquifers beneath aquitards of at least 2 years of vertical travel time are adequately protected because vertical flow in the aquitards is only 0.7 m per year. The most sensitive parameters are virus attachment and inactivation. The next most sensitive parameters are grain size of the sand, abstraction rate of groundwater, virus concentrations in raw sewage and consumption of unboiled drinking water. Research is recommended on additional protection by attachment and under unsaturated conditions.

Models, Biological↗

PubMed2006

A random search methodology for examining parametric uncertainty in water quality models.

The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values.

Algorithms↗