Bayesian inference

Phylogeny-Guided Global-Local Shrinkage for Microbiome Feature Selection

This paper introduces PGL, a Bayesian regression method for microbiome feature selection that uses phylogenetic trees to improve detection of exposure-associated ASVs.

Unravelling genomic and environmental contributions to nitrogen-use efficiency in tropical maize through Bayesian causal forests

This paper uses Bayesian causal forests to estimate the causal effect of nitrogen levels on maize traits, finding that environmental factors largely drive trait variation while genomic background influences baseline levels.

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.

Fixing the Measurement Model Before the Selection Heuristics: Optimiser Repairs for Living-Cell Self-Driving Laboratories

This paper compares measurement-aware models and selection-level repairs in Bayesian optimisation for living-cell experiments, finding that measurement-aware models reduce regret and improve coverage under data contamination.

Bayesian Stacking of Quantitative Adverse Outcome Pathways Isolates Primary Drivers of Liver Steatosis

This paper develops a Bayesian framework to integrate multiple converging pathways in adverse outcome pathways, demonstrating improved predictive performance for liver steatosis.

pyreml: a general-purpose, GPU-accelerated mixed model solver in Python

pyreml is a GPU-accelerated Python library for linear mixed models that outperforms existing closed-source REML software in speed and functionality.

A clinically annotated proteomic and histological resource of 1,000 colorectal cancers

Deep proteomics of 997 treatment-naïve colorectal cancers defines six subgroups, splitting chromosomally unstable tumors into three and revealing a rare adverse subgroup, with H&E slide benchmarks.

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

Unattended droplet-microfluidic Bayesian optimization of a glycerol detection cascade matched design of experiments (DoE) with one third of the conditions; a second cascade used DoE with Raman readout.

Simulation-free approximate Bayesian computation for stochastic reaction networks

This paper introduces SFABC, a simulation-free method for Bayesian inference of stochastic reaction networks, showing it matches simulation-based accuracy at lower computational cost.

abcneuralnet: Simulation-based Inference with Bayesian Neural Networks and Explainable AI for Population Genomics Inference

abcneuralnet provides a Bayesian Neural Network framework with explainable AI for population genomics inference, offering calibrated prediction intervals and uncertainty decomposition.

CaPri: Bayesian Mendelian randomization for joint estimation of causal and pleiotropic effects

CaPri is a Bayesian Mendelian randomization framework that jointly estimates causal and pleiotropic effects, showing robust performance across varying pleiotropy proportions and real data applications.

Joint Variable Selection for Omic Biomarkers in Time-to-Event Data

The paper introduces vampW, a Bayesian method for joint variable selection in omic biomarkers and time-to-event data, showing reduced false discoveries and improved prediction.

Efficient approximate Bayesian inference for large, heterogeneous biological datasets

The paper introduces an adaptive minibatch approach for approximate Bayesian inference, improving speed-precision tradeoffs for large, heterogeneous biological datasets.

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.

The limits of Bayesian estimates of divergence times in measurably evolving populations

This paper examines the limits of Bayesian divergence time estimates in microbial evolution, finding that uncertainty scales with distance to calibration points rather than absolute age.

Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy

StrainSpy fits zero-inflated beta mixed models to containment ANI, reaching >98% simulated sensitivity versus ~79% for logistic regression and detecting colorectal-cancer E. coli strain shifts.

BOSE: A Bayesian Order Statistics-Based Estimator for Recovering the Sample Mean and Standard Deviation from the Median, Range, and/or Interquartile Range

BOSE estimates sample mean and standard deviation from median, range, and interquartile range using Bayesian order statistics, showing robustness and improved performance in small samples and non-normal distributions.

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.

Socio-cognitive models in a patch foraging setting: a case study for model selection and parameter identifiability methods

The paper investigates socio-cognitive mechanisms in collective foraging using agent-based models and evaluates model selection and parameter identifiability through Bayesian inference and Wasserstein distance.

Full-Support Conditional Clade Distribution for Bayesian Tree Topology Testing

This paper introduces KRegCCD, a new Bayesian phylogenetic model that improves posterior tree estimation and enables topology testing by providing full support over tree space.

Fast pace of life is associated with host status in small mammals, despite pervasive surveillance bias

Arenavirus and hantavirus surveillance in small mammals tracks night-lights and accessibility; after adjusting for effort, fast-lived and synanthropic species are somewhat more often detected as hosts.

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.

HGFX: a validated Python/JAX reproduction of the Hierarchical Gaussian Filter toolbox

The paper presents HGFX, a Python/JAX implementation of the Hierarchical Gaussian Filter that matches a MATLAB reference implementation in behavior and numerical results.

New perspectives in assessing environmental risks for birds: a simple TKTD framework to link growth and reproduction energy budget to chemical stress

The paper introduces BIRDkiss, a TKTD framework linking growth and reproduction energy budgets to chemical stress in birds, showing good calibration with observed data and highlighting food availability's role in reproductive outcomes.

OpenCRS: an open-source regulated human cardiorespiratory model with large-scale calibration and global sensitivity analysis at rest and during exercise

OpenCRS is a cardiorespiratory model calibrated with global sensitivity analysis to identify influential parameters and mechanisms during rest and exercise.

sabinaMBM: An R package for Multiscale Bayesian species distribution Modelling using INLA

The paper introduces sabinaMBM, an R package for multiscale Bayesian species distribution modeling that improves out-of-sample discrimination and uncertainty propagation across scales.

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

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.

Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models

The paper introduces CASTIE, a method for context-dependent eQTL mapping in single-cell data, identifying regulatory variants missed by static models and linking them to disease traits.

Towards reconstruction of the human interactome from positive and negative experimental evidence

Inferring tested-but-undetected pairs from prey lists of 5,530 published interaction screens yields 97 million tested human protein pairs and 18,982,508 non-interactions (≥3 tests) for calibrating screens and predictors.