Bayesian inference

Bayesian prediction of multivariate ecology from phenotypic data yields new insights into the diets of extant and extinct taxa

The paper predicts multivariate ecology from phenotypic data, showing Bayesian models accurately predict diet rankings in carnivorans and provide new insights into extinct species.

Statistical Assessment of Biomarker Replicability using MAJAR Method

The paper introduces MAJAR, a statistical framework to assess biomarker replicability in meta-analysis, showing improved power and robustness compared to existing methods.

Bayesian Inference of a Spectral Graph Model for Brain Oscillations

This paper develops a Bayesian procedure for inferring spectral graph model parameters using simulation-based inference, showing comparable performance to annealing algorithms while enabling uncertainty quantification.

A Bayesian Regularized and Anotation-Informed Integrative Analysis of Cognition (BRAINIAC)

BRAINIAC estimates variance explained by features for a cognitive phenotype and assesses annotation impact on feature enrichment using resting state fMRI data.

Skyline fossilized birth-death model is robust to violations of sampling assumptions in total-evidence dating

This paper shows that the skyline FBD model provides more robust age estimates in total-evidence dating by reducing deep root attraction effects caused by sampling biases.

Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

The paper introduces a scalable Bayesian method for phylogenetic tree inference from single-cell whole genome sequencing data, demonstrating its application on three datasets.

Applying Hierarchical Generalized Additive Models to Non-Compartmental Analysis of Pharmacokinetic Data

The paper applies hierarchical generalized additive models to non-compartmental pharmacokinetic analysis, enabling covariate effects and censored responses in a Bayesian framework.

Estimating temporally variable selection intensity from ancient DNA data II

This paper introduces a Bayesian framework for inferring time-varying selection from ancient DNA data, improving upon previous methods by addressing issues with allele age dependencies.

Predictive power of non-identifiable models

The paper demonstrates that non-identifiable models can still have predictive power when measured with appropriate stimulation protocols and iterative variable measurements.

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

Baymer is a Bayesian hierarchical model that estimates mutation probabilities using sequence context, showing improved accuracy and robustness compared to previous methods.

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

The paper introduces a flexible empirical Bayes method for multivariate multiple regression to improve prediction of multi-tissue gene expression from genotypes.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data

This paper develops a Bayesian method to infer host-pathogen interaction matrices from genome-wide polymorphism data and applies it to HCV-infected humans to identify new resistance genes and GxG interactions.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 93 risk genes for Alzheimer's disease dementia

This paper applies Bayesian genome-wide TWAS incorporating cis- and trans-eQTL from brain and blood tissues to identify 93 risk genes for Alzheimer's disease dementia.

Unified Tumor Growth Mechanisms from Multimodel Inference and Dataset Integration

The paper uses Bayesian multimodel inference to analyze tumor growth mechanisms in SCLC, finding that high lineage plasticity, not rare stem-like populations, drives tumor evolution.

PrePPI: A structure informed proteome-wide database of protein-protein interactions

PrePPI predicts protein-protein interactions on a proteome-wide scale using structural and non-structural clues, providing a database of ~1.3 million human PPIs.

CoPheScan: phenome-wide association studies accounting for linkage disequilibrium

CoPheScan improves PheWAS by accounting for linkage disequilibrium, identifying causal associations and reducing false positives in genetic variant analyses.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

This paper introduces a multilevel Bayesian HMM to analyze longitudinal neural recordings, identifying stable latent states linked to behavioral events in macaque motor cortex.

Uncovering Ghost Introgression Through Genomic Analysis of a Distinct East Asian Hickory Species

The paper detects ghost introgression in Carya sinensis using genomic analysis and phylogenetic methods, revealing introgression from an extinct lineage.

Bayesian Multivariate Longitudinal Model for Immune Responses to Leishmania: a tick-borne Co-Infection Study

This paper develops a Bayesian state-space model to analyze immune responses to Leishmania infections in dogs, finding that co-infection and prior pathogen load influence disease progression.

Genetic fine-mapping from summary data using a non-local prior improves detection of multiple causal variants

This paper introduces FiniMOM, a Bayesian fine-mapping method that improves detection of multiple causal variants using non-local priors and summary data from GWAS.

A model-based hypothesis framework to define and estimate the diel niche via the 'Diel.Niche' R package

The paper introduces a Bayesian modeling framework and R package to quantitatively define and estimate diel niches using four hypothesis sets for diel phenotypes.

Bayesian analysis dissects kinetic modulation during non-stationary gene expression

The paper uses Bayesian methods to analyze kinetic transcription parameters in yeast under stress, revealing fast bursting dynamics within slower cycles.

Bayesian optimization for design of multiscale biological circuits

This paper introduces a Bayesian optimization method for designing multiscale biological circuits, demonstrating its effectiveness on gene circuits with nonlinearities and multiple scales.

BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing

BBmix improves genotype calling from RNA-sequencing by modeling RNA-specific biases, achieving higher accuracy in heterozygous calls compared to existing methods.

Genetic insights into ossification of the posterior longitudinal ligament of the spine

This study identifies genetic loci and causal traits associated with ossification of the posterior longitudinal ligament of the spine using GWAS and Mendelian randomization analyses.

Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional bursting

The paper introduces a Bayesian model to analyze 4sU scRNA-seq data, improving inference of transcriptional bursting parameters and their relationship with histone modifications.

Functional dissection of neural connectivity in generalized anxiety disorder

This study uses Bayesian and frequentist methods to analyze resting-state fMRI data from females with GAD, finding reduced functional connectivity in specific brain regions.

The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling

This paper uses integrative structural modeling to determine the molecular architecture of the desmosomal outer dense plaque and identifies structural features related to mechanical stress resilience and disease.

Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries

This paper uses a Bayesian language model to design high-affinity scFv antibody libraries, achieving 28.8-fold better binding than directed evolution.

Perceptual decisions exhibit hallmarks of dynamic Bayesian inference

The paper shows human perceptual decisions align with optimal Bayesian inference in dynamic environments, with decision bias depending on context reliability, environmental stability, and time since context change.