Bayesian inference

This study shows that excluding low-frequency variants and indels in fine-mapping leads to false discoveries and introduces SuSiE-OUT to address this issue.

This paper uses single-cell eQTL-MR and colocalization analysis to identify immune cell-specific genetic mechanisms associated with Alzheimer's disease risk.

This study investigates how the multiple-demand network maintains working memory during task interruptions, finding that neural representations of category information remain stable despite behavioral costs.

This study investigates the effects of natural daylight and physical activity on circadian phase using a Bayesian crossover experiment, finding inconclusive results.

The paper introduces the segmentome, a DNA methylome representation using a Bayesian HMM, and shows it can group cell types and identify cell-of-origin in DNA samples.

The paper identifies RGMB as a potential risk factor for East Asian POAG through proteogenomic analysis, showing ancestry-specific associations and lack of colocalization with IOP traits.

The paper identifies immune-cell-specific genetic associations and drug targets in prostate cancer using integrative analyses of single-cell and bulk data.

SPARK reconstructs enhancer-based gene regulatory networks using single-cell multiomics data and prior knowledge, revealing regulatory links associated with immune disease GWAS variants.

Deep proteomics of 997 treatment-naïve colorectal cancers defines six subgroups, splitting chromosomally unstable tumors into three and revealing a rare adverse subgroup, with H&E slide benchmarks.

abcneuralnet provides a Bayesian Neural Network framework with explainable AI for population genomics inference, offering calibrated prediction intervals and uncertainty decomposition.

This paper develops a Bayesian framework to accurately infer cell-size regulation strategies by accounting for noise and variability, showing that previous methods were biased and that adder-like regulation is more consistent across organisms.

The paper introduces vampW, a Bayesian method for joint variable selection in omic biomarkers and time-to-event data, showing reduced false discoveries and improved prediction.

The paper assesses covariance stationarity in spatial transcriptomics and identifies spatially varying genes using non-stationary covariance models.

The paper develops a Bayesian gene interaction network to identify 13 genes for PD classification, achieving high AUC in a blood-based transcriptomic analysis of the PPMI cohort.

This paper investigates the genetic link between retinal arteriolar tortuosity and blood pressure, identifying potential shared genetic loci and suggesting retinal microvasculature as a candidate window for inner-ear microvasculature studies.

This paper applies Bayesian hierarchical modeling to assess regional brain atrophy in rare neurodegenerative disorders, finding significant volume loss in specific brain regions across disease groups.

The paper compares the rate of decline in gene-level evolutionary conservation and human constraint scores with disease onset age across Mendelian diseases.

BREATH simultaneously infers phylogenetic and transmission trees for outbreaks using sequence data, improving resolution and origin timing estimates in a tuberculosis outbreak.

Factorizing multiple monotone pseudotimes into one shared donor permutation plus cluster-specific warps (HiP-BIND) yields Alzheimer's donor orderings agreeing better with Braak, ADNC and CERAD staging than single-trajectory B-BIND.

LGTM couples a neural topic model with additive basis-function Gaussian processes, learning microbial co-abundance topics and how covariates relate to their trajectories in longitudinal gut microbiome data.

The paper introduces a Bayesian method for estimating population-level brain connectivity from small samples, showing improved accuracy over existing methods in simulations and real data.

StrainSpy fits zero-inflated beta mixed models to containment ANI, reaching >98% simulated sensitivity versus ~79% for logistic regression and detecting colorectal-cancer E. coli strain shifts.

SFUMATO provides a Bayesian probabilistic framework for spatial transcriptomics that captures uncertainty and enables continuous visualization of transcriptional domains.

The paper evaluates computational frameworks to prioritize pathogenic variants in deafness-associated genes, identifying 28,866 VUSs with high pathogenicity probability and improving diagnoses for 12 patients.

This paper develops normative models to detect deviations in molecular-enriched functional connectivity associated with amyloid burden in healthy aging brains.

The paper introduces ISPat-3D to estimate conditional cell population associations in 3D tissue images, finding stable associations in colorectal cancer but less stable in breast cancer.

This study identifies shared genetic factors between GERD and psychiatric disorders, showing stronger links with internalizing disorders than with BMI.

This study develops a Bayesian framework to analyze NK cell cytotoxicity and identifies distinct mechanisms underlying variability in killing responses.

TCF25 is mostly cytoplasmic with a perinuclear membrane-proximal pool, stabilises GPRASP2 against proteasomal degradation, and its C-terminal residues 604-664 are required for their association.

The paper introduces a Bayesian framework to infer latent infection risk patterns from virus neutralisation assay titration data, revealing higher and more persistent antibody concentrations for EV-D68 compared to other viruses.