Bayesian inference

Integration of individual and population data to improve predictions of structured population dynamics

This paper develops a Bayesian dynamical IPM framework that integrates population density and individual growth data to improve predictions of size-structured population dynamics under global change.

Systematic Integration of genomics with transcriptomics for the Study of Coronary Artery Disease and Subclinical Atherosclerosis

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

A time-resolved axillary volatile fingerprint of human fear

The paper identifies a molecular fingerprint of acute human fear using axillary volatile emissions and a combination of analytical techniques.

A Dynamical Digital Twin Unmasks Hidden Neuromotor Control Policies and Catastrophic Tipping Points in Parkinson's Disease

The paper develops a dynamical digital twin framework to uncover hidden neural control policies and catastrophic tipping points in Parkinson's disease by analyzing sway kinematics and Bayesian inference.

Dissecting the Genetic Link Between Insulin Resistance and Coronary Heart Disease: A Multi-Trait GWAS and Proteomic-Metabolomic Mediation Analysis

This paper identifies a proteomic and metabolomic pathway linking insulin resistance to coronary heart disease through genetic analysis and mediation modeling.

Compartment-dependent selection regimes maintain the G-quadruplex paradox across the eukaryotic kingdoms

The paper investigates the G4 paradox across eukaryotic genomes, finding compartment-dependent selection regimes that maintain G4 structures in regulatory regions and suppress them in coding sequences.

A hyperspherical deep Bayesian model for interpretable clustering and relationship prediction in microbiome multi-omics integration

DBayesCM integrates multi-omics microbiome data using a deep Bayesian model to enable interpretable clustering and relationship prediction across diseases.

Super-resolved imaging and Bayesian analysis of single exocytosis events reveals molecular-scale patterning by cortical microtubule arrays

The paper uses super-resolution imaging and Bayesian analysis to show cortical microtubules create spatial patterns that organize exocytosis in plant cells.

Functional brain network correlates of pubertal timing and depressive symptoms in preadolescence

This study examines how pubertal timing relates to resting-state functional connectivity in preadolescents and its potential link to depressive symptoms.

HiBASIL: Hierarchical Bayesian Source Inference and Localization for Spatial Epidemiology

HiBASIL accurately localizes multiple sources in spatial epidemiology, handling unbalanced foci and sparse data with high precision.

A Bayesian Study to Optimally Unveil LDH Kinetic Mechanisms In Vivo

This study uses Bayesian statistics and mathematical modeling to efficiently characterize lactate dehydrogenase kinetics in vivo and in vitro.

Kalman-filter Force Inference: an estimation framework for cellular forces from temporal evolution of epithelial morphogenesis

This paper introduces Kalman-filter Force Inference (KFI) to estimate cellular forces from time-lapse imaging of epithelial deformation, without assuming static force balance.

Acute opioid responses are modulated by dynamic interactions of Oprm1 and Fgf12

This study identifies time-dependent epistatic interactions between Oprm1 and Fgf12 that modulate opioid-induced locomotion in mice and links Fgf12 to opioid behavior in humans.

Towards Bayesian-based quantitative adverse outcome pathways using in vitro data from open literature and continuous variables: a case example for liver fibrosis.

This paper develops a Bayesian-based model using in vitro data and continuous variables to predict liver fibrosis by analyzing key markers of hepatic stellate cell activation.

Quantifying target antigen-dependent CAR T-cell performance against AML

The paper develops a mathematical model and Bayesian framework to evaluate CAR T-cell efficacy against AML, revealing antigen-specific resistance mechanisms.

Bayesian Network Analysis Identifies PREX1 as a Master Regulator of Cytoskeletal Disruption in Inflammatory Bowel Disease

Dynamic Bayesian networks for neural information flow:evaluation of continuous and discrete scoring metrics

This study evaluates Bayesian network scoring metrics and other methods for analyzing neural information flow in simulated single-neuron and population data, finding discrete dynamic Bayesian networks to be the most consistent.

GPSNorm: Gaussian Process Spatial Normalization for Spatial Transcriptomics

GPSNorm fits slide, spatial technical and biological effects in one Bayesian negative binomial model, carrying normalization uncertainty into differential expression and lowering log-fold change error in simulations.

Beyond Invariable Sites: Using Evolutionary Stasis to Map Multi-Layered Constraints on the Evolution of Viral and Mammalian Genomes

This paper introduces B-STILL, a Bayesian framework to detect functional constraints in genomes by analyzing evolutionary stasis, identifying significant clusters of invariant sites in viral and mammalian genomes.

Power and linkage disequilibrium differences are major confounders of human eQTL portability across ancestries and cohorts

This paper shows that differences in power and linkage disequilibrium affect eQTL portability across ancestries and introduces a method to correct for these factors.

Resolving FRET Signal Degeneracy and Population Heterogeneity via Bayesian Nonparametrics

This paper introduces BNP-FRET-Bin to resolve FRET signal degeneracy and population heterogeneity by inferring state numbers and kinetics directly from FRET data, revealing new biologically relevant states.

RETROFIT: REFERENCE-FREE DECONVOLUTION OF CELL-TYPE MIXTURES IN SPATIAL TRANSCRIPTOMICS

RETROFIT deconvolves cell-type mixtures in spatial transcriptomics without requiring references, outperforming existing methods and matching reference-based approaches when references are imperfect.

Improving performance of polygenic risk scores for hypertension across two ancestry groups.

This paper compares two Bayesian methods for polygenic risk scores (PRS) for hypertension across two ancestry groups, finding improved performance with updated LD panels and functional annotations, but not equitable results between groups.

Integrative structure determination of a human mitochondrial contact site and cristae organizing system (MICOS) sub-assembly

The paper determines the structure of a MICOS sub-assembly using integrative methods, revealing novel interfaces and mutation hotspots.

A hierarchical clock-mixture model for Bayesian phylogenetic dating

This paper introduces a hierarchical clock-mixture model for Bayesian phylogenetic dating to better handle uncertainty in clock choice and improve timescale estimation.

Bayesian Factor Analysis for Binary and Ordinal Phenotypes with Missingness

This paper introduces FABOr, a Bayesian framework for matrix factorization of binary and ordinal phenotypes, showing improved imputation accuracy on real-world data.

Experimentally Tuned Protein-RNA Rosetta Score Function using Bayesian Optimization

The paper develops an experimentally tuned protein-RNA score function for ROSETTA using Bayesian Optimization to improve energetic agreement with experimental data.

Experimentally calibrated computational modeling of inflammation and vascular remodeling in breast cavity healing following breast-conserving surgery

This study develops a computational model of inflammation and vascular remodeling in breast cavity healing after lumpectomy, calibrated with experimental data to predict healing outcomes.

A Bayesian method for estimation of plant soil water content with application to low-cost horticultural robotics

The paper develops a Bayesian method to estimate soil water content from capacitive sensor data, enabling low-cost plant watering systems without destructive calibration.

UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell

UQ-PhysiCell provides a Python framework for uncertainty quantification, calibration, and model analysis in PhysiCell simulations, enabling scalable and reproducible uncertainty-aware analysis of agent-based models.