Bayesian inference

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy

This paper introduces Bayesian methods for Mendelian randomization using summary statistics to address correlated pleiotropy in both univariable and multivariable settings.

No Evidence of Altered Language Laterality in People Who Stutter across Different Brain Imaging Studies of Speech and Language

This study examines language lateralization in people who stutter using fMRI and finds no evidence of altered lateralization compared to typically fluent speakers.

Bayesian evidence for the neural dissociation between finger and hand imitation skills

The paper uses Bayesian and frequentist analyses to assess neural dissociation between finger and hand imitation deficits in stroke patients, finding biases in pooled samples.

Bayesian 13C-metabolic flux analysis of parallel tracer experiments in granulocytes: A directional shift within the non-oxidative pentose phosphate pathway supports phagocytosis

The paper uses Bayesian 13C-metabolic flux analysis to show phagocytosis reverses non-oxidative PPP flux direction in granulocytes.

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images

ESQmodel evaluates single-cell segmentation quality using expression data and a prior belief of cellular composition to compute per-cell entropy.

An integrated Bayesian framework for multi-omics prediction and classification

The paper introduces a Bayesian ensemble method for multi-omics prediction and classification, showing improved performance and uncertainty quantification across four datasets.

Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models

This paper develops Bayesian generalized linear mixed models to estimate HLA adaptation in HIV-1 and finds that CXCR4-using variants are more adapted than CCR5-using variants.

Development of selection strategies for genetic improvement in production traits of Mecheri sheep based on a Bayesian multi trait evaluation

This study uses Bayesian analysis to estimate genetic parameters for production traits in Mecheri sheep and proposes weaning weight as a selection criterion for improving growth traits.

Polymorphism-aware models in RevBayes: Species trees, disentangling Balancing Selection and CG-biased gene conversion

This paper introduces PoMoBalance, a polymorphism-aware model to disentangle balancing selection and GC-biased gene conversion using genomic data from Drosophila populations.

A Bayesian Approach to Non-Metric Hyperbolic Multi-Dimensional Scaling

This paper introduces a Bayesian non-metric hyperbolic MDS method to embed data in hyperbolic space, showing robustness to hyperparameters and capturing non-linear structures.

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

This paper introduces BayesDeep, a Bayesian model that reconstructs spatial transcriptomics data at single-cell resolution using histology images and SRT datasets.

Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq

The paper introduces consensus scHPF, a method to identify robust gene signatures from scRNA-seq data, and applies it to discover cell type-specific drug responses in glioma.

Regional Genetic Architecture of the Corpus Callosum and its Overlap with Psychiatric and Substance Use Phenotypes

The paper investigates the genetic architecture of the corpus callosum and its overlap with psychiatric and substance use phenotypes using GWAS summary statistics and causal mixture models.

Multiscale Bayesian Simulations Reveal Functional Chromatin Condensation of Gene Loci

This paper introduces Hi-C metainference, a Bayesian method to model chromatin organization, revealing that gene loci in mouse embryonic stem cells are dynamically condensed and organized as crumpled globules.

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

The paper introduces FGES-Merge, a method for learning large gene regulatory networks from brain data, achieving high accuracy and speed while providing interpretable results.

Improving fine-mapping by modeling infinitesimal effects

This paper introduces improved fine-mapping methods that model infinitesimal effects, showing better calibration and increased PRS accuracy for causal variant identification in complex traits.

Probabilistic ecological risk assessment for deep-sea mining: a Bayesian Network for Chatham Rise, SW Pacific Ocean

This paper applies Bayesian Networks to assess ecological risks of deep-sea mining on Chatham Rise, identifying uncertainties and informing policy decisions.

Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding

The paper distinguishes between Bayesian Encoding and Decoding frameworks, showing they can coexist and explain neural activity differently.

TREVI: A Transcriptional Regulation-driven Variational Inference Model to Speculate Gene Expression Mechanism with Integration of Single-cell Multi-omics

TREVIXMBD integrates scRNA-seq and scATAC-seq data to model gene regulation, improving cell typing and batch correction while revealing TF-gene regulation variations.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

rvTWAS integrates gene expressions with rare variant sequences to identify gene-trait associations in psychiatric disorders.

CryoENsemble - a Bayesian approach for reweighting biomolecular structural ensembles using heterogeneous cryo-EM maps

CryoENsemble improves conformational ensembles from molecular dynamics simulations to better match cryo-EM data and extract dynamics information.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

Inference for entomological semi-field experiments: Fitting a mathematical model assessing personal and community protection of vector-control interventions

The paper develops a Bayesian inference method to assess personal and community protection of vector-control interventions using semi-field experimental data.

Nunchaku: Optimally partitioning data into piece-wise linear segments

The paper introduces a Bayesian algorithm to optimally partition 2D data into piece-wise linear segments, applied to microbial growth analysis.

A calibration and uncertainty quantification analysis of classical, fractional and multiscale logistic models of tumour growth

This study evaluates classical, fractional, and multiscale logistic models of tumour growth using Bayesian inference and uncertainty quantification to assess their fit under different treatment conditions.

Flexible Perception of Tactile Cues in Multiple Reference Frames

This study shows how reference frame demands shape touch perception by integrating tactile and proprioceptive cues in a task-specific manner, with faster reaction times and lower decision thresholds in a Sternum-centric task.

Mental image reconstruction from human brain activity

This study improves image reconstruction from brain activity by incorporating semantic information, enabling reconstruction of both seen and imagined images.

A Model-Based Clustering via Mixture of Bayesian Hierarchical Models with Covariate Adjustment for Detecting Differentially Expressed Genes from Paired Design

This paper introduces a Bayesian hierarchical mixture model with covariate adjustment to detect differentially expressed genes in paired design whole-genome data, showing improved performance over existing methods.

Inferring disease architecture and predictive ability with LDpred2-auto

This paper validates LDpred2-auto for inferring genetic parameters and shows improved heritability capture and predictive performance with an expanded variant set.

Fast Bayesian inference of phylogenies from multiple continuous characters

The paper develops Bayesian methods to estimate phylogenies using continuous morphological characters and validates them through simulations and diverse datasets.