Bayesian inference

Genetic, demographic and clinical variables act synergistically to impact neurodevelopmental outcomes in children with single ventricle heart disease

The paper examines how genetic, demographic, and clinical factors interact to influence neurodevelopmental outcomes in children with single ventricle heart disease.

Simulation-based inference for efficient identification of generative models in connectomics

This paper introduces simulation-based Bayesian inference to efficiently identify generative models in connectomics by inferring wiring rule parameters from empirical data.

Detecting episodic evolution through Bayesian inference of molecular clock models

This paper introduces a Bayesian method to detect episodic evolution by comparing molecular clock models and applies it to SARS-CoV-2 data, finding strong evidence for episodic evolution.

Active vision in binocular depth estimation: a top-down perspective

This paper proposes an active inference framework for depth estimation that combines action-perception cycles and Predictive Coding principles to improve depth belief accuracy through biologically plausible neural circuits.

Bayesian analysis of resting-state temporal low-γ power in children with phonemic decoding deficits and certain comorbidities

The paper uses Bayesian analysis to examine low-γ power in children with phonemic decoding deficits, finding no support for hypothesized effects on left hemisphere power or lateralization, but a left-dominance in RD when attentional deficits are excluded.

A probabilistic view of protein stability, conformational specificity, and design

The paper defines protein stability and conformational specificity probabilistically and introduces BayesDesign, a sequence decoding algorithm that maximizes p(structure|seq) for protein design.

An accurate probabilistic step finder for time-series analysis

This paper introduces BNP-Step, a Bayesian nonparametric method for step-finding in noisy time-series data, which accurately identifies transitions and emission levels without assuming geometric holding times or using ad hoc penalties.

Uncovering specific mechanisms across cell types in dynamical models

This paper introduces a regularization method for identifying cell-type specific mechanisms in dynamical models, demonstrating its effectiveness with synthetic and biological data.

Bayesian Maximum Entropy Ensemble Refinement

This paper introduces a Bayesian method to refine molecular dynamics simulations using nuclear Overhauser effect measurements by estimating maximum entropy parameters through adaptive Markov Chain Monte Carlo methods.

Inference of multiple mergers while dating a pathogen phylogeny

This paper develops a method to detect multiple merger events in pathogen phylogenies and improves dating by incorporating these events, validated on simulated and real datasets.

Pleiotropic Genetic Effects between Multiple Sclerosis and Musculoskeletal Traits

This study identifies genetic correlations and causal relationships between multiple sclerosis and musculoskeletal traits using GWAS data and Mendelian Randomization.

Bayesian phylogenetics on globally emerging SARS-CoV-2 variant BA.2.86 suggest global distribution and rapid evolution

The paper analyzes SARS-CoV-2 variant BA.2.86 genomes to estimate its emergence date and spread using Bayesian phylogenetics and bioinformatics methods.

Bayesian optimization of separation gradients to maximize the performance of untargeted LC-MS

This paper introduces BAGO, a Bayesian optimization method for optimizing LC gradients in untargeted LC-MS, improving compound annotation and spectral purity.

The functional impact of rare variation across the regulatory cascade

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

Single-Molecule Reaction-Diffusion

This paper introduces Bayes-smRD, a method to analyze single-molecule reaction-diffusion using photon arrival data, revealing molecule-specific properties with reduced data and photodamage.

Bayesian Additive Regression Trees for Genotype by Environment Interaction Models

The paper introduces AMBARTI, a Bayesian additive regression tree model for genotype by environment interactions in plant genetics, showing it is competitive or superior to existing methods.

Probabilistic Learning of Treatment Trees in Cancer

The paper introduces a Bayesian probabilistic framework to infer treatment trees from PDX data, identifying synergistic treatment combinations and their biological mechanisms across cancers.

Critical role of the periplasm in copper homeostasis in Gram-negative bacteria

This study investigates the role of the periplasm and periplasmic proteins in copper homeostasis in Gram-negative bacteria, showing that the periplasm acts as a sensor and storage for copper to adapt to varying environmental concentrations.

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

This paper evaluates Bayesian Linear Regression models with BayesC and BayesR priors as fine mapping tools, comparing them to existing methods and showing improved F1 scores and credible set properties.

Bayesian statistics improves biological interpretability of metabolomics data from human cohorts

The paper applies Bayesian statistics to metabolomics data from human ME/CFS cohorts, revealing more consistent and biologically meaningful metabolic differences compared to frequentist methods.

Online Bayesian Optimization of Nerve Stimulation

This paper develops a Bayesian optimization framework for real-time neural stimulation parameter optimization, achieving personalized dosing with fewer stimulations and uncertainty-aware safety.

Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle

The paper quantifies how gene expression and splicing QTL explain heritability in cattle traits using Bayesian methods across 16 tissues.

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

SAMBA models fish gut microbiome interactions using Bayesian networks to infer how farming conditions affect microbiome diversity and predict changes under experimental variables.

Nuclear genetic control of mtDNA copy number and heteroplasmy in humans

Blood whole genomes from 274,832 people yield 92 independent nuclear signals for mtDNA copy number and 42 for inherited heteroplasmy levels, implicating mtDNA replication machinery.

Bayesian Tests for Random Mating in Polyploids

The paper introduces Bayesian methods to test for random mating in autopolyploids, improving upon frequentist approaches by handling small samples and genotype uncertainty.

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

This study compares Biophysical Network Models (BNMs) with different delay specifications to determine their role in generating alpha-band phase synchronization networks in MEG data.

Bridging stimulus generalization and representation learningvia rational dimensionality reduction

The paper proposes a Bayesian model linking stimulus generalization and representation learning through dimensionality reduction, supported by experimental findings.

Computations and neural dynamics of audiovisual causal and perceptual inference in schizophrenia

This study investigates perceptual and causal inference in schizophrenia using psychophysics, Bayesian modeling, and EEG, finding intact computational mechanisms in medicated patients.

Model-based inference of RNA velocity modules improves cell fate prediction

The paper introduces cell2fate, a model-based RNA velocity method that improves cell fate prediction by decomposing velocity solutions into modules and applying it to human brain development data.

Constraint and innovation in color evolution among species and among plumage patches in five avian radiations

The paper analyzes color evolution across five bird clades, finding that new color mechanisms enable new color space occupancy but not increased diversity, and that interpatch variation diverges from interspecies trends in males.