Bayesian inference

Assessing the inference of single-cell phylogenies and population dynamics from CRISPR lineage recordings

This paper evaluates the accuracy of inferring cell phylogenies and population dynamics from CRISPR lineage recordings using simulations and Bayesian methods.

A Hierarchical Bayesian Agent Based Model for Binary Spatio-Temporal Spread: Theory, PDE Scaling Limit, and an Application to Predator Prey Cycles

The paper develops a hierarchical Bayesian agent-based model for binary spatio-temporal data and shows its scaling to a PDE, applying it to predator-prey cycles.

Data aggregation and mechanistic modeling enable dose-response analysis of SARS-CoV-1 in non-human primates

The paper develops a Bayesian model to aggregate NHP challenge study data and estimate SARS-CoV-1 infectious dose, enabling dose-response analysis despite limited data from individual studies.

Comparing Neuroprotective Drug Efficacy in Rodent Neonatal Brain Injury Models

This study uses an adaptive design to compare four neuroprotective drugs in rodent neonatal brain injury models, identifying azithromycin and erythropoietin as the most effective.

Replaying germinal center evolution on a quantified affinity landscape

Across 119 monoclonal germinal centers, B cell affinity rises reliably despite divergent phylogenies. Selection is noisy but persistent; mutability limits exploration. A DMS-based landscape shows saturating fitness and survivorship biases.

Adaptive Bayesian localization of motor representation areas

This paper introduces an adaptive Bayesian method for localizing motor representation areas using TMS, reducing the number of stimuli needed for accurate localization.

Big data analysis of animal movements in aquatic ecosystems with acoustic telemetry

The paper applies Bayesian inference to large acoustic telemetry datasets to improve movement pattern analysis and residency estimation in aquatic ecosystems.

A Bayesian approach to interpret time-resolved experiments using molecular simulations

This paper introduces a Bayesian method to interpret time-resolved experiments using molecular simulations, demonstrating its application to protein unfolding studies.

Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data

spaGSE improves pathway enrichment analysis in spatial transcriptomics by incorporating spatial dependence, identifying biologically relevant pathways in cancer and developmental tissues.

Bayesian generative modeling reveals a multi-modal hierarchical architecture in the mouse functional connectome

The paper uses Bayesian generative modeling to show the mouse brain's functional connectome has multiple co-dominant organizational configurations rather than a single structure.

Overweight status drives early tumor microenvironment reprogramming in pancreatic ductal adenocarcinoma: a cell-type-resolved Bayesian hierarchical modeling and interactome analysis

This study identifies how overweight status reprograms the pancreatic tumor microenvironment through cell-type-specific changes using Bayesian modeling and interactome analysis.

Bayesian Inference of Bond Parameters from Interactions between Single Filaments

The paper uses Bayesian inference to determine bond parameters between vimentin filaments from force trajectories measured with optical tweezers.

Bayesian Parameter Balancing Enables Robust and Consistent Estimation of Kinetic Parameter Uncertainty

The paper introduces a Bayesian parameter balancing framework to estimate kinetic parameter uncertainty with robustness and consistency across studies.

Multi-resolution Spatial Graphical Regression Models for Hierarchical Spatial Transcriptomics Data

This paper introduces a Bayesian multi-resolution spatial graphical regression framework to infer spatially varying gene networks from hierarchical spatial transcriptomics data, showing improved network recovery and identifying key regulatory hubs in kidney cancer.

A Bayesian approach to include Indigenous Knowledge in habitat selection functions

This paper develops a Bayesian method to integrate Indigenous Knowledge into habitat selection models for ringed seals, demonstrating its impact on conservation decisions.

The phylodynamic threshold of measurably evolving populations

This study shows that determining if a population is measurably evolving or has reached the phylodynamic threshold depends on model assumptions and sampling strategies, not just data, and emphasizes prior sensitivity over tests of temporal signal.

Bayesian optimisation and graph-based rheology enable sequence-dependent modelling of DNA materials

This paper uses Bayesian optimisation and graph-based rheology to model DNA materials by linking sequence design to material properties.

What did the dove sing to Pope Gregory? Ancestral melody reconstruction in Gregorian chant using Bayesian phylogenetics.

The paper reconstructs ancestral Gregorian chant melodies using Bayesian phylogenetics and compares results to historical methods.

The Hypothesis Race Model for evaluation of research findings

The paper introduces the Hypothesis Race Model as a Bayesian approach to improve the evaluation of research findings by addressing limitations of Null Hypothesis Significance Testing.

R-package Jsmm: Joint species movement modelling of mark-recapture data

The paper introduces the R-package Jsmm for analysing mark-recapture data using Bayesian joint species movement modelling to estimate movement behaviours and population parameters.

High frequency spike inference with particle Gibbs sampling

This paper introduces a Bayesian method using particle Gibbs sampling to infer high-frequency neuronal spikes and quantify their uncertainty, showing improved performance on benchmark datasets.

Structural distance at the tRNA synthetase active site interface predicts pathogenicity but is captured by AlphaMissense and EVE except among score-ambiguous variants

The paper investigates if structural distance to the tRNA-binding interface improves missense variant pathogenicity prediction, finding that existing models like AlphaMissense and EVE already capture this information.

Evolutionary transitions to self-fertilization influence the inference of introgression history

This study shows that transitions to self-fertilization can mislead introgression inferences, and coalescent statistics help correct these biases in demographic models.

Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in Post-COVID-19 Syndrome

This paper identifies altered dynamic brain states in Post-COVID-19 Syndrome using resting-state fMRI, showing reduced attentional states and increased limbic-DMN states linked to cognitive impairment and inflammation.

Resolving inflammatory bowel disease risk variants to genes and cell types

This study identifies 619 IBD risk signals, resolves 81 high-confidence variants, and prioritizes 664 candidate effector genes using GWAS, eQTL, and exome sequencing.

Bayesian Estimation of Mosaic Loss of Chromosome Y from Bulk RNA Sequencing Data

This paper develops a Bayesian method to estimate mosaic loss of chromosome Y from bulk RNA-seq data, showing it can detect large LOY events with moderate accuracy.

Temporal-difference valence-partitioned Bayesian brains work out whether others are caring or uncaring

The paper introduces a Caring Attributions task and identifies valence-partitioned temporal-difference Bayesian inference as a new neurocomputational mechanism for judging others' care or lack thereof.

A New Sparse Bayesian Quantile Neural Network-based Approach and Its Application to Discover Physiological Sweet Spots in the Canadian Longitudinal Study on Aging

The paper introduces Q-FSNet and Q-DirichNet, sparse Bayesian neural networks for identifying physiological sweet spots in metabolomic data from the Canadian Longitudinal Study on Aging.

Categorical Bayes Filtering for Computational Phenotyping in Adaptive Learning

The paper introduces a deterministic Bayesian filtering method to disentangle environmental volatility and observation stochasticity in adaptive learning, revealing distinct phenotyping patterns.

Resting-state heartbeat-evoked potentials are associated with Kalman-derived cardiac prediction errors

This study uses a Kalman filter to estimate cardiac prediction errors from resting-state EEG and ECG data and finds they are associated with heartbeat-evoked potential (HEP) amplitude.