Bayesian inference

Multi-sample, multi-platform isoform quantification using empirical Bayes

This paper introduces JOLI, a hierarchical model that improves RNA isoform quantification by integrating short-read and long-read data across multiple samples.

Distinguishing new from persistent infections at the strain level using longitudinal genotyping data

The paper develops DINEMITES, a statistical method to distinguish new from persistent infections using longitudinal genotyping data, showing improved accuracy over simple rules.

Dynamics of influenza transmission in vampire bats revealed by longitudinal monitoring and a large-scale anthropogenic perturbation

The paper uses longitudinal data and a cull event to show vampire bats maintain influenza virus independently of fruit bats and that culling reduces transmission.

BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture

BESTDR quantifies drug response mechanisms using Bayesian modeling of cell state transitions, improving accuracy in dose-response analysis across cell lines.

A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score

This paper introduces a Bayesian method to correct attenuation bias in regression analysis using polygenic risk scores, showing improved coefficient estimation and credible interval coverage.

The Sequential Multispecies Coalescent

This paper introduces a Sequential Monte Carlo method for species tree inference under the multispecies coalescent model, showing comparable performance to existing Bayesian MCMC approaches.

Phylodynamic estimation of the within-host mutation rate of extended-spectrum beta-lactamase-producing Enterobacterales

The paper estimates within-host mutation rates of ESBL-producing E. coli and K. pneumoniae using phylodynamic models on longitudinal genomic data from hospitalized patients.

Approaching Maximum Resolution in Structured Illumination Microscopy via Accurate Noise Modeling

This paper introduces B-SIM, an unsupervised Bayesian framework for structured illumination microscopy that improves resolution and contrast by accurately modeling noise, outperforming existing methods in both simulated and experimental data.

A Bayesian decision-making model of implicit motor learning from internal and external errors

This paper introduces a Bayesian model to explain how humans distinguish and adapt to internal vs. external movement errors during motor learning.

Unraveling latent cognitive, metacognitive, strategic, and affective processes underlying children's problem-solving using Bayesian cognitive modeling

This paper develops a Bayesian model to uncover latent cognitive and affective processes in children's arithmetic problem-solving and shows it predicts academic outcomes and mediates the effects of anxiety.

Time-lapse Image Super-resolution Neural Network with Reliable Confidence Quantification for Optical Microscopy

This paper introduces a time-lapse image super-resolution neural network with confidence quantification for optical microscopy, improving temporal consistency and reliability.

A-TWAS: An aggregated transcriptome-wide association study model incorporating multiple Bayesian priors

A-TWAS improves transcriptome-wide association studies by integrating multiple Bayesian priors, enhancing predictive accuracy and identifying more disease-relevant genes.

Shared and distinct molecular effects of regulatory genetic variants provide insight into mechanisms of distal enhancer-promoter communication

The paper identifies trimodal QTLs that jointly affect enhancer accessibility, promoter contacts, and gene expression in monocytes, revealing genetic links between enhancer activity and chromatin boundaries.

Bayesian Optimization of Neurostimulation (BOONStim)

BOONStim uses Bayesian optimization to improve TMS targeting by integrating fMRI and TMS simulation tools, showing faster convergence and accuracy compared to grid optimization.

Bayesian inference of fitness landscapes via tree-structured branching processes

FiTree infers fitness landscapes from single-cell tumor mutation trees using Bayesian methods, outperforming existing models and identifying epistatic effects in leukemia data.

Decoding Demosponge Diversity: Bayesian Analysis of Biodiversity, Extinction Events and Environmental Influences throughout the Phanerozoic

This study uses a Bayesian framework to analyze fossil data and identify extinction events and environmental influences on demosponge diversity throughout the Phanerozoic.

Bayesian p-curve Mixture Models as a Tool to Dissociate Effect Size and Effect Prevalence

This paper introduces a Bayesian method to estimate effect prevalence and size by clustering participant data, outperforming existing methods when effect size is uncertain.

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

This paper introduces VBI, a toolkit for Bayesian inference on virtual brain models, demonstrating its accuracy and reliability through in-silico testing.

Navigating the peptide sequence space in search for peptide binders with BoPep

BoPep identifies peptide binders to specific proteins by efficiently navigating sequence space, reducing docking evaluations and discovering novel therapeutic peptides.

A Bayesian framework for ranking genes based on their statistical evidence for differential expression

This paper introduces a Bayesian framework for differential gene expression analysis that ranks genes based on statistical evidence rather than binary classification, improving transparency and computational efficiency.

Effects of Centrally Acting Analgesics on Resting-State Electroencephalography Biomarker Candidates of Chronic Pain

This study investigates the effects of centrally acting analgesics on rsEEG biomarkers of chronic pain and finds no significant medium to large effects.

Multiple Types of Context-Specific Brain Causal Regulatory Networks and their Applications to Autism Spectrum Disorder

The paper constructs context-specific brain gene regulatory networks from bulk and single-cell RNA-seq data using Bayesian and deep learning methods to study Autism Spectrum Disorder mechanisms.

A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data

This paper introduces IBSEP, a statistical framework that integrates bulk and scRNA-seq data to prioritize cell-type-specific eQTLs, showing improved performance over existing methods.

Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease

This study identifies cell-type-specific druggable targets in Alzheimer's disease by integrating GWAS and eQTL data across multiple cell types and datasets.

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

edgeR v4 adds a continuous negative binomial for fractional counts, bias-corrected quasi-likelihood for small counts, C-based fitting, and new transcript, exon, methylation and fold-change-threshold analyses.

Testing for changes in population trends from low-cost ecological count data

This paper introduces birp, a Bayesian tool for testing population trend changes in ecological data, showing it can accurately detect trends even with noisy or sparse data.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Canopy2 infers tumor phylogeny using bulk DNA and single-cell RNA sequencing data, outperforming existing methods in reconstructing clonal trees.

A Bayesian active learning platform for scalable combination drug screens

BATCHIE efficiently identifies effective drug combinations through adaptive experimentation, reducing the number of required experiments while accurately predicting synergies.

Integrated phenomenology and brain connectivity demonstrate changes in nonlinear processing in jhana advanced meditation

This study examines neural connectivity during advanced meditation using EEG and phenomenological ratings, finding that nonlinear dynamics better distinguish meditation states than linear metrics.

Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control

This paper shows that combining hierarchical Bayesian modeling with multivariate decoding improves the reliability and precision of estimating individual differences in cognitive control from fMRI data.