Bayesian inference

BEATRICE: Bayesian Fine-mapping from Summary Datausing Deep Variational Inference

BEATRICE identifies causal variants from GWAS summary data using a Bayesian framework, outperforming baselines in coverage and finding the APOEε2 allele in Alzheimer's disease.

Family-GWAS reveals effects of environment and mating on genetic associations

Family-based GWAS meta-analysis of 34 phenotypes shows standard GWAS effects carry sizeable confounding uncorrelated with direct genetic effects, while assortative mating likely inflates SNP heritability and genetic correlations.

MOIRE: A software package for the estimation of allele frequenciesand effective multiplicity of infection from polyallelic data

MOIRE, a Bayesian R package, jointly infers allele frequencies, multiplicity of infection (MOI) and within-host relatedness from SNP or polyallelic malaria genotypes, yielding a panel-portable effective MOI.

Aladynoulli: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction

ALADYNOULLI integrates longitudinal health records and genetic data to model disease progression, identifying interpretable disease signatures with cross-population consistency and improving clinical prediction.

Separating biological variance from noise by applying EM algorithm to modified General Linear Model

The paper introduces EMSEV, a method using EM algorithm on modified GLM to separate biological variance from noise in biological data, showing moderate accuracy in parameter estimation under varying conditions.

Skeletons in the Forest: Using Entropy-Based Rogue Detection on Bayesian Phylogenetic Tree Distributions

The paper introduces entropy-based rogue detection algorithms to improve Bayesian phylogenetic analysis by identifying and removing rogue taxa and clades that distort tree distributions.

Mixture Models for Dating with Confidence

This paper introduces a mixture model approach for divergence time estimation that improves precision and robustness compared to traditional methods while reducing computational demands.

BayeSMART: Bayesian Clustering of Multi-sample Spatially Resolved Transcriptomics Data

BayeSMART improves multi-sample spatial clustering by integrating AI-reconstructed single-cell data with spatial context, outperforming existing methods in accuracy and efficiency.

Leveraging Transcriptomics-Based Approaches to Enhance Genomic Prediction: Integrating SNP weights and gene-networks for Cotton Fibre Quality Improvement

This paper improves genomic prediction accuracy for cotton fibre traits by integrating gene-network clusters derived from RNA-seq data into Bayesian models.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data

CSP_Rank improves protein-peptide complex structure modeling by integrating AlphaFold2 with NMR chemical shift perturbation data through Bayesian conformational selection.

Evaluation of Bayesian Point-Based System on the Variant Classification of Hereditary Cancer Predisposition Genes

The paper evaluates a Bayesian point-based system for variant classification in hereditary cancer genes, finding it reduces variants of uncertain significance compared to ACMG/AMP 2015 guidelines.

Gene-level alignment of single cell trajectories

Genes2Genes aligns single-cell trajectories using a Bayesian DP framework, revealing gene-level mismatches and highlighting differences in T cell differentiation between in vitro and in vivo states.

Randomised Badger Culling Trial lacks evidence for proactive badger culling effect on tuberculosis in cattle: comment on Mills et al. 2024, Parts I & II

The paper re-evaluates statistical analyses of the Randomised Badger Culling Trial, finding no effect of culling on cattle tuberculosis incidence and supporting the 2006 conclusion that badger culling is unlikely to help control cattle TB.

A unified hierarchical Bayesian approach to transcriptome-wide association study

This paper introduces a unified Bayesian approach for transcriptome-wide association studies (TWAS) that improves accuracy by jointly modeling transcriptome and GWAS data, showing better performance in gene-trait association detection.

Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina

This study shows fixational eye movements improve retinal signal precision by enhancing spike timing and cell interactions in macaque retinas.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BayesianSSA integrates environmental information from perturbation data into structural sensitivity analysis to predict enzyme perturbation responses in metabolic networks.

Climatic-environmental influences on hominin brain size over the last 5 million years

This study uses Bayesian phylogenetic methods to analyze hominin fossil data and finds that colder and more variable temperatures positively influence brain size evolution within species, but this effect weakens in later Homo species.

Precision not prediction: Body-ownership illusion as a consequence of online precision adaptation under Bayesian inference

The paper presents a computational model showing that body-ownership illusions arise from precision adaptation in Bayesian inference during sensory stimulation.

Bayesian clustering with uncertain data

This paper introduces DPMUnc, a Bayesian clustering method that incorporates data uncertainty, and applies it to GWAS and gene expression data to identify disease subgroups.

Mapping Cell-to-cell Interactions from Spatially Resolved Transcriptomics Data

The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.

Does transcutaneous vagus nerve stimulation alter pupil dilation? A living Bayesian meta-analysis

This paper performs a Bayesian meta-analysis comparing continuous and pulsed tVNS protocols on pupil dilation, finding stronger evidence for pulsed tVNS effects.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Reanalysis of public environmental metatranscriptomes from the Huanan market links SARS-CoV-2 positivity to one wildlife stall containing raccoon dog, civet and bamboo rat genetic material.

DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

DIISCO infers dynamic intercellular interactions from time-series single-cell data using a Bayesian framework, showing its ability to capture time-dependent communication patterns in simulated and CAR-T cell data.

Bayesian Optimized sample-specific Networks Obtained ByOmics data (BONOBO)

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

Extensive co-regulation of neighbouring genes complicates the use of eQTLs in target gene prioritisation

The paper benchmarks methods for prioritizing GWAS target genes using eQTLs and finds that assigning pQTLs to closest protein-coding genes outperforms colocalisation methods.

Substantial role of rare inherited variation in individuals with developmental disorders

The paper investigates the role of rare inherited coding variants in developmental disorders by analyzing family and case/control data, finding significant burdens of damaging variants and estimating their population attributable risk.

Stepwise Bayesian Machine Learning Uncovers a Novel Gene Regulatory Network Component in Neural Tube Development

The paper uses a stepwise Bayesian framework to identify a novel gene regulatory network component in neural tube development using whole-genome sequencing data from neural precursor cells treated with Sonic Hedgehog.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

This paper introduces a dynamic generative model to extract interpretable oscillatory components from multichannel neurophysiological recordings, improving component selection and interpretation.

A Novel Methodology to Recalibrate Pathogenic Range of SCA36 Repeat Expansions for PGT-M

This paper recalibrates the pathogenic range of SCA36 repeat expansions using Bayesian methods and long-read sequencing to improve clinical applicability for preimplantation genetic testing.

Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning

This paper introduces a hierarchical Bayesian Augmented Hebbian Reweighting Model to model perceptual learning at both individual and population levels, showing significant computational improvements.