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PubMed2025

Bayesian estimation of allele-specific expression in the presence of phasing uncertainty.

MOTIVATION: Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. RESULTS: We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. AVAILABILITY AND IMPLEMENTATION: The software is freely available from Github (https://github.com/x811zou/BEASTIE); and Zendo (DOI: 10.5281/zenodo.15062124).

Bayes Theorem↗
PubMed2025

Bayesian reconstruction and differential testing of excised introns.

MOTIVATION: Characterizing the differential excision of introns is critical for understanding the functional complexity of a cell or tissue, from normal developmental processes to disease pathogenesis. Most transcript reconstruction methods infer full-length transcripts from high-throughput sequencing data. However, this is a challenging task due to incomplete annotations and the heterogeneous expression of transcripts across cell-types, tissues, and experimental conditions. Several recent methods circumvent these difficulties by considering local splicing events, but these methods lose transcript-level splicing information and may conflate similar, but distinct transcripts. RESULTS: In this work, we formalize a new transcript reconstruction problem that interpolates between the full-length and local splicing perspectives by considering sequences of exon-exon junctions (SEEJs) that co-occur in transcripts. We then present a hierarchical Bayesian admixture model and posterior inference algorithms for computing SEEJs (BSEEJ), and a generalized linear model for characterizing differential SEEJ usage based on model parameter estimates. We show that BSEEJ achieves high F1 score for reconstruction tasks and improved accuracy and sensitivity in differential splicing when compared with six transcript and local splicing methods on simulated data. Lastly, we evaluate BSEEJ on experimental data based on transcript reconstruction, novelty of transcripts produced, model sensitivity to hyperparameters, and a functional analysis of differentially expressed SEEJs. AVAILABILITY AND IMPLEMENTATION: BSEEJ is freely available at https://github.com/bayesomicslab/BSEEJ.

Bayes Theorem↗
PubMed1978

Computer-assisted identification of anaerobic bacteria.

A computer program was developed to identify anaerobic bacteria by using simultaneous pattern recognition via a Bayesian probabilistic model. The system is intended for use as a rapid, precise, and reproducible aid in the identification of unknown isolates. The program operates on a data base of 28 genera comprising 238 species of anaerobic bacteria that can be separated by the program. Input to the program consists of biochemical and gas chromatographic test results in binary format. The system is flexible and yields outputs of: (i) most probable species, (ii) significant test results conflicting with established data, and (iii) differential tests of significance for missing test results.

Anaerobiosis↗
PubMed2026

Genomic background of gestation length and calving-related traits in Holstein cattle.

The reproductive success of cows directly influences the profitability of dairy farms. Reproductive traits, particularly calving-related traits, generally have low heritability but sufficient additive genetic variance to enable genetic progress through genomic selection. Thus, the primary objectives of this study were to estimate genetic parameters and perform single-step genome-wide association studies (ssGWAS) for calf size, calving ease, gestation length, and stillbirth in Holstein cattle. Variance components were estimated based on animal models and Bayesian inference using a data set containing 226,717 animals with phenotypic records, 15,761 animals genotyped with 45,101 SNP markers, and 461,819 animals in the pedigree. SNP effects were estimated using the single-step GBLUP method. For direct and maternal genetic effects, heritability estimates (posterior standard deviation) ranged from 0.001 (0.002) for gestation length in heifers to 0.16 (0.001) for gestation length in cows. Genetic correlations ranged from -0.57 (0.01) between calving ease and stillbirth in heifers to 0.74 (0.01) between gestation length evaluated in heifers and cows. The ssGWAS results supported a highly polygenic architecture for calving-related traits, with most genomic signals not reaching genome-wide significance. A genome-wide significant association was detected for calving ease in cows on BTA23, highlighting FARS2 as a positional candidate gene. The strongest GWAS signals for each trait harbored additional biologically important candidate genes, including NPPA, NPPB, BCHE, EPHA4, DLD, and GTF2I. Given the generally low heritability estimates and the predominantly polygenic architecture observed for these traits, genomic selection may contribute to the genetic improvement of calving-related traits in Holstein cattle, with potential benefits for cow welfare, calf survival, and overall dairy production efficiency.

dairy cattle↗
PubMed1988

Transplantation statistics in the UK--an agenda for the next quinquennium.

Our next quinquennial plan for transplantation studies extends MPI to corneal and unrelated marrow transplantation. It applies Bayesian hierarchical modelling to regional variation in donor procurement and continues a program of special studies to augment national databases with respect to kidney, corneal, heart and liver transplantation. It promotes research collaboration among European and other organ exchange organizations as pioneered in the Council of Europe 1986 Study on High Sensitization, which showed the effectiveness of the network of European organ exchange organizations in liaising with transplant units.

Bayes Theorem↗
PubMed1987

Suicide risk prediction by computer interview: a prospective study.

A computer interview program that uses a subjective Bayesian probability model to assess suicide risk was evaluated. Predictions made by clinicians for 52 patients were compared with predictions made by the computer for the same patients. The computer was significantly (p = .001) better at predicting attempters, and clinicians were significantly (p = .01) better at predicting nonattempters. An analysis of receiver operating characteristic curves showed that the computer had better overall discrimination, but the difference was nonsignificant.

Decision Making, Computer-Assisted↗
PubMed1984

Clinical inferences and decisions--II. Decision trees, receiver operator curves and subjective probability.

In patient management, clinical decisions follow a logical sequence which can be formally expressed as a decision tree in which the uncertainties associated with each alternative outcome may be made explicit using Bayes' theorem. Where test data is used in the formulation of a decision, the uncertainty associated with the information it conveys may be modified by changing the pass/fail criterion to alter the false positive and false negative error rate. Classical procedures based on information theory are described to illustrate how this may be achieved for any test. When hard data is not available to permit such an approach, the clinician must rely on his own past experience or that of a colleague. Several methods are available for quantifying such experience by estimating subjective probabilities associated with an action or test result. Two simple methods are described for deriving subjective probabilities for subsequent use within a Bayesian decision model.

Bayes Theorem↗
PubMed1981

Case-control diagnosis and Bayesian inference in common viral infections.

The predictive values of symptoms and signs for given diseases are often unknown. The fact that a high proportion of individuals with a certain disease may have a specific group of symptoms (the case-control approach) does not necessarily mean that the specific group of symptoms will allow one reliably to diagnose the disease. This study, utilizing a population based data set for common acute infections, shows that descriptions of common viral illnesses found in medical textbooks that associate illnesses with symptoms do not allow one to predict reliably isolation of the supposed causal organism. Positive predictive value of groups of symptoms for specific viral infections did not exceed 11 percent in this study. However, the data closely fitted the Bayesian statistical model often proposed for such decision making by physicians.

Adolescent↗
PubMed2025

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Humans↗
PubMed2025

Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.

With the rapid development of sequencing technology, the application of genomic prediction has become more and more common in breeding schemes of livestocks and crops. Selecting an appropriate statistical model is of central importance to achieve high prediction accuracy. Recently, machine learning models have been expected to upgrade genomic prediction into a new era. However, the perspective still suffers from lack of evidence that machine learning models can generally outperform the traditional ones on empirical data sets. In this study, we compared two machine learning models based on artificial neural network (ANN) and convolutional neural network (CNN) with four traditional models, including genomic best linear unbiased prediction (GBLUP), Bayesian ridge regression (BRR), BayesA and BayesB, using three published data sets for grain yield in wheat. For each model, we considered two variants: modeling and ignoring the genotype-by-environment ([Formula: see text]) interaction. In the comparison, we considered two strategies of cross-validation: predicting genotypes that have not been evaluated in any environment (CV1) and predicting genotypes that have been tested in other environments (CV2). Our results showed that traditional Bayesian models (BayesA, BayesB, and BRR) outperformed GBLUP, ANN and CNN when considering [Formula: see text] interaction. The accuracies of ANN and CNN were higher than traditional models only in CV1 and when [Formula: see text] interaction was ignored. It was also found that the performance of the two machine learning models was significantly affected by the interaction between the CV strategy and the way of treating the [Formula: see text] interaction, while that of the four traditional models was only influenced by whether the [Formula: see text] interaction was considered or not. Thus, machine learning models can be a powerful complementary to the traditional ones and their superiority may depend on the prediction scenario. Among the two machine learning models, we observed that the accuracy of ANN was higher than CNN in most cases, indicating that it is still challenging to adapt complex machine learning models such as CNN to genomic prediction.

ANN↗
PubMed2026

Internal Bayesian precision modulates the neural representation of social attention: Disentangling implicit and explicit components via model-informed multivariate EEG analysis.

Social attention integrates sensory cues with high-level cognitive expectations, yet the generative mechanisms through which implicit orienting and explicit belief-driven modulation interact remain poorly understood. This ambiguity complicates the distinction between specialized social modules and domain-general attentional processes. We combined a dynamic cueing task with hierarchical Bayesian modeling and model-informed multivariate EEG decoding to address this. Behavioral results revealed a computational double dissociation: symbolic arrow cues elicited heterogeneous strategies, whereas averted gaze recruited a consistent, surprise-driven computational phenotype. At the neural level, time-resolved decoding and temporal generalization revealed a critical representational shift starting approximately 400 ms post-cue. Initial activity related to physical cue features was rapidly replaced by stable neural templates of predicted spatial intent. Crucially, topographical activation patterns showed that this intentional template, characterized by a lateralized temporo-occipital distribution, emerged exclusively under high internal certainty. Furthermore, partial representational similarity analysis demonstrated that late-stage neural manifolds were overwhelmingly organized around integrated spatial goals rather than isolated sensory or motivational signals. These findings suggest that social attention is a specialized generative process, where internal certainty modulates the transformation of social perceptions into actionable top-down intentions.

Bayesian computational modeling↗
PubMed1988

[Bayesian estimates of unknown parameters of mathematical models of the dynamics of the mutation process and changes in the ratio of cells having passed different numbers of divisions in a culture].

With the help of Bayesian methods, the conditions of solving experimental data samples and their divisions were established, equivalence of estimations of unknown linear dynamic models parameters proved, the estimations having been worked out by both accounting calculation errors and using their compensations with additional noise in the original model's discrete analog. The results are used in mathematical modeling of changing intensity of the process of hereditary pathology frequencies, and the process of changing the ratio of cells having passed different numbers of divisions in the culture.

Bayes Theorem↗
PubMed1982

Bayesian approach for a nonlinear growth model.

Nonlinear least squares methods are currently used for fitting a well-known growth model, namely the Jenss model, to the length measurements of a child followed throughout the first six years of life. An empirical Bayes approach is developed for fitting the model, and the prior distribution of the growth-model parameters is estimated from a large sample of least squares parameters. An expression which is proportional to the posterior distribution is derived so that the posterior mode can be estimated. Given the observations on a child, this posterior mode provides Bayes estimates of the Jenss curve parameters for the child.

Bayes Theorem↗
PubMed1991

Multivariate analysis of cardiovascular reflexes applied to the diagnosis of autonomic neuropathy.

A battery of cardiovascular reflex tests is usually performed for the diagnosis of autonomic neuropathy. The tests discriminate well between normal and definitely abnormal autonomic function. However, in some patients the results are borderline and their autonomic status cannot be better defined. We performed multivariate statistical analysis of six cardiovascular autonomic tests with the aim of increasing their diagnostic efficiency. Eighty-five healthy subjects and 95 patients at risk for autonomic neuropathy were studied. Principal component analysis and two pattern recognition methods, the Bayesian technique and the SIMCA method, were applied. It was found that: (1) normal models obtained by Bayesian analysis showed very high specificity and sensitivity; (2) a battery of two tests for parasympathetic function (R-R interval variation test, deep breathing) and two tests for sympathetic function (blood pressure responses to standing and to sustained handgrip) provide an appropriate diagnostic approach, if multivariate analysis is used; (3) multivariate analysis allows a more precisely defined assessment of autonomic nervous system function in so-called borderline patients.

Adult↗
PubMed1992

Bayesian analysis of stochastic constraints in structural equation models.

Structural equation models are analysed in the presence of stochastic constraints. Based on a Bayesian perspective, a prior distribution on nuisance parameters in the unknown covariance matrix of error measurements with stochastic constraints is considered. An iterative procedure is implemented to produce the various Bayesian estimates with stochastic constraints. A simulation study is conducted to illustrate the accuracy and behaviour of this Bayesian approach. A real-life example is provided to illustrate the theory.

Bayes Theorem↗
PubMed2025

Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.

The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.

COVID-19↗
PubMed2025

Diagnosing the undiagnosed: AI-enhanced multimodal modeling for placental mesenchymal dysplasia in high-risk pregnancies.

Placental mesenchymal dysplasia (PMD) is a rare vascular placental disorder that mimics molar pregnancy but often coexists with a viable fetus, making its misdiagnosis potentially devastating. In high-risk pregnancies, artificial intelligence (AI)-enhanced multimodal modeling - incorporating imaging, genomics, proteomics, and clinical features - offers a transformative diagnostic strategy. Leveraging Bayesian hyperparameter optimization for model refinement, this approach improves diagnostic accuracy while reducing uncertainty and clinician hesitation. Recent clinical studies support its efficacy and interpretability through SHAP and LIME models, while real-time surgical enhancements using Bayesian methods highlight its broader clinical utility. Despite current challenges such as data heterogeneity and integration barriers, multimodal AI provides unprecedented resolution in placental analysis, enabling precise differentiation between PMD and similar fetopathies. Ultimately, this advancement supports timely, non-invasive diagnosis, personalized management, and emotionally informed decision-making aligned with ethical AI implementation standards.

Bayesian optimization↗