Bayesian inference

Soft Sensing of Intracellular States for CHO Cell Bioprocessing with Ensemble Kalman Filters

This paper uses Ensemble Kalman Filters to estimate intracellular nucleotide sugar donors in CHO cell cultures from extracellular measurements, improving metabolic state inference.

Inferring the multi-host fitness landscape of endive necrotic mosaic virus from cross-inoculation experiments

The paper infers a multi-host fitness landscape of endive necrotic mosaic virus using cross-inoculation experiments and a Bayesian model linked to Fisher's geometrical model.

Making Biorisk Measurable: A Bayesian Framework for Laboratory Risk Management

This paper introduces a Bayesian framework for laboratory risk management that quantifies biosafety risk using a log-risk scale and incorporates WHO Risk Group classifications and incident escalation models.

LGTM: Gaussian Process Modulated Neural Topic Modeling for Longitudinal Microbiome

LGTM couples a neural topic model with additive basis-function Gaussian processes, learning microbial co-abundance topics and how covariates relate to their trajectories in longitudinal gut microbiome data.

Analyzing multisensory integration: dos and donts

The paper argues that behavioral measures of multisensory integration are composite and misinterpreted, proposing a model-based inference pipeline to disentangle underlying factors.

Empirical-Bayes and Bayesian Hierarchical Modelling forMissingness and Differential Expression in Proteomics

The paper introduces a hierarchical Bayesian model to handle missing data and improve differential expression analysis in proteomics by accounting for uncertainty in missing values.

sandwrm: an R package for estimating Wright's neighborhood size and species-level genetic diversity

The paper introduces an R package called sandwrm to estimate neighborhood size and species-level genetic diversity using a Bayesian approach based on the Wright-Malecot model.

The prevalence of protein misfolding as a mechanism for hereditary deafness

The paper evaluates computational frameworks to prioritize pathogenic variants in deafness-associated genes, identifying 28,866 VUSs with high pathogenicity probability and improving diagnoses for 12 patients.

A directed TM->JM coupling in receptor tyrosine kinase dimers, set by activating mutations and the membrane environment

The paper uses MD simulations and computational methods to determine the direction of conformational coupling in RTK dimers, showing it correlates with receptor activity states.

Estimating conditional cell population associations across tumor density trajectory in three dimensional tissue images

The paper introduces ISPat-3D to estimate conditional cell population associations in 3D tissue images, finding stable associations in colorectal cancer but less stable in breast cancer.

Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data

The paper introduces TOPAZ, a framework that uses topological data analysis and Bayesian methods to infer agent-based models from spatiotemporal cellular data, showing it can accurately recover model parameters and distinguish between models.

decemedip: hierarchical Bayesian modeling for cell type deconvolution of immunoprecipitation-based DNA methylomes

The paper introduces decemedip, a Bayesian hierarchical model for cell type deconvolution of MeDIP-seq data, showing its accuracy and utility in identifying methylation signatures.

SmartHisto: Bayesian Active Learning for Histology Images

SmartHisto reduces annotation effort for histopathology images using Bayesian active learning, achieving higher mean IoU than competing methods.

Estimating protein isoform abundances with PAQu

PAQu estimates protein isoform abundances using multiomic data, outperforming existing methods and revealing increased C4A isoform levels in schizophrenia.

Vascular waveform analysis using Bayesian pulse deconvolution

This paper introduces Bayesian pulse deconvolution to improve vascular waveform analysis by jointly solving signal filtering, pulse timing detection, and pulse shape extraction, achieving significant error reductions in simulations and real-world data.

Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State

This paper introduces a Bayesian method for inferring gene regulatory networks using a Chemical Langevin Equation model, enabling structure identification and kinetic parameter estimation without observing transient dynamics.

Macroevolutionary Rates of Species Interactions: Approximate Bayesian Inference from Cophylogenies

This paper introduces an ABC method to infer speciation rates in cophylogenetic systems, finding host speciation is the fastest in a beetle-mimic commensalism dataset.

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies

FM-GPT is a Bayesian fine mapping method that identifies causal genes in phenome-wide transcriptome-wide association studies, improving accuracy and reducing false positives.

HiCPotts: An R/Bioconductor package to identify significant interactions in chromosome conformation capture data and model sources of biases.

HiCPotts is an R/Bioconductor package that identifies significant chromatin interactions in Hi-C data while modeling biases using a zero-inflated Negative Binomial distribution.

Causal inference for multiple risk factors and diseases from genomics data

The paper introduces CI-GWAS, a method to improve causal inference in GWAS by addressing confounding and pleiotropy, finding few suitable IVs and replicating some but not all previously reported causal links.

Bayesian model discovery for reverse-engineering biochemical networks from data

This paper introduces a Bayesian sparse likelihood-free inference method to reverse-engineer biochemical networks from single-cell data, showing high accuracy and outperforming correlation-based methods.

Benchmarking Bayesian colocalization methods in validating Mendelian randomization-identified targets

This study benchmarks four Bayesian colocalization methods to validate MR-identified protein-trait associations for cardiometabolic traits, finding SharePro most frequently supports significant associations.

Harmonizing Peak Matching Between Multidimensional NMR Spectra

This paper develops an algorithm to automate cross-peak matching between multidimensional NMR spectra by modeling deviations as a distribution of noise and true shifts, using Bayesian inference to provide confidence estimates.

Simulation-based Bayesian deep learning enables uncertainty-aware tumor fraction estimation in cell-free DNA

This paper introduces purNPE, a Bayesian deep learning framework for estimating tumor fraction in cell-free DNA without labeled cancer samples, achieving comparable performance to existing methods.

Uncertainty-aware breeding decisions: MCMC-based optimum contribution selection increases breeding decision robustness

The paper introduces CVaR-OCS, a method that improves breeding decisions by incorporating uncertainty in genetic evaluations, leading to more robust selection outcomes.

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.

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.