Bayesian inference

Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes

This paper introduces a method to classify gene-by-treatment interactions using Bayesian model selection and applies it to molecular count phenotypes with a software package.

LIET Model: Capturing the kinetics of RNA polymerase from loading to termination

The paper introduces the LIET model to analyze RNA polymerase kinetics across transcription stages and applies it to human cell-types and perturbation experiments to detect changes in transcription dynamics.

Leveraging Bayesian Networks for Consensus Network Construction and Multi-Method Feature Selection to Decode Disease Prediction

The paper introduces CMIMN, a Bayesian network method for microbiome network inference and a consensus approach to improve reliability, identifying key taxa associated with potato common scab disease.

A blood- and brain-based EWAS of smoking

The paper conducts a Bayesian EWAS and GWAS of smoking using DNA methylation data from blood and brain tissues, identifying smoking-related loci and developing an epigenetic biomarker.

Particle algorithms for animal movement modelling in autonomous receiver networks

This paper introduces a Bayesian filtering-smoothing algorithm for animal movement modelling in autonomous receiver networks, showing it outperforms heuristic methods in accuracy and flexibility.

Protein Sequence Modelling with Bayesian Flow Networks

This paper introduces ProtBFN, a Bayesian Flow Network for protein sequence generation, which outperforms existing models and is fine-tuned for antibody generation.

Bayesian nonparametric analysis of residence times for protein-lipid interactions in Molecular Dynamics simulations

The paper uses Bayesian nonparametric methods to analyze residence times of cholesterol interactions with GPCRs in MD simulations, revealing kinetic rates and molecular events.

REPOP: bacterial population quantification from platecounts

REPOP reconstructs bacterial populations from platecounts by accounting for dilution and plating uncertainties, revealing hidden multimodality that standard methods obscure.

Model-based tumour subclonal deconvolution accounting for spatio-temporal sampling biases

This paper introduces MOBSTERm, a Bayesian framework for subclonal deconvolution that accounts for spatio-temporal sampling biases in cancer sequencing data, improving the analysis of tumour evolution.

Timing and clustering co-occurring genome amplifications incancers

This paper introduces TickTack, a Bayesian model to time and cluster co-occurring genome amplifications in cancer, revealing insights into tumor evolution and genomic instability.

Bayesian inference by visuomotor neurons in prefrontal cortex

This study shows that prefrontal cortex neurons integrate sensory signals and prior expectations to perform Bayesian inference during perceptual decisions.

Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaks

Delphy provides a fast, scalable Bayesian phylogenetic analysis tool for outbreak tracking, achieving 1,000x speedups while maintaining state-of-the-art accuracy.

Structural Evaluation of RYR2-CPVT Missense Variants and Continuous Bayesian Estimates of their Penetrance

The paper evaluates RYR2-CPVT missense variants using Bayesian penetrance analysis and finds it outperforms existing tools.

Inferring cell dynamics in stress-induced neuroblastoma cell cultures

The paper quantifies cell dynamics and interactions in neuroblastoma cell cultures under stress, finding that cell-to-cell interactions drive phenotypic adaptation, which accelerates with lower nutrient levels.

Bayesian inference of genetic pleiotropy identifies drug targets and repurposable medicines for human complex diseases

The paper uses a Bayesian model to identify shared genetic components across complex diseases, finding potential drug targets with lower side effects and repurposing opportunities.

Interictal discharges spread along local recurrent networks between tubers and surrounding cortex

This study links interictal epileptiform discharge spread to network coupling in tuberous sclerosis patients using EEG and neural mass modeling.

Phylogenetic modelling of compositional and exchange rate changes over time

This paper models compositional and exchange rate heterogeneity in molecular evolution and compares their detection using Bayesian models and matched pairs tests.

Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders

The paper identifies placental DNA methylation (DNAm) as a potential mediator of genetic risk for schizophrenia, bipolar disorder, and major depressive disorder using mQTL and GWAS data.

bayesTPC: Bayesian inference for Thermal Performance Curves in R

The paper introduces bayesTPC, an R package for Bayesian inference of thermal performance curves, aiming to improve uncertainty quantification in thermal physiology.

Inference and visualization of complex genotype-phenotype maps with gpmap-tools

gpmap-tools uses Gaussian processes to infer and visualize genotype-phenotype maps with higher-order epistasis; across 262,144 Shine-Dalgarno variants it reveals register-specific peaks linked by three-nucleotide-offset ridges.

Likelihood of social-ecological genetic model

This paper proposes a probabilistic framework integrating genetic and social-ecological processes to model species distribution and demographic history using coalescent methods and Bayesian inference.

Avian-specific Salmonella transition to endemicity is accompanied by localized resistome and mobilome interaction

This study examines the evolution of the resistome in Salmonella Gallinarum during its transition to endemicity, revealing localized mobilome-driven resistance profiles.

But the clock, tick-tock: the preeminence of relaxed clock models in total-evidence dated phylogenetics

This study evaluates the impact of relaxed clock models on total-evidence dated phylogenetics using echinoid data, showing their influence on divergence times and macroevolutionary inferences.

Precision-based causal inference modulates audiovisual temporal recalibration

The paper investigates audiovisual temporal recalibration by comparing observer models and finds that causal inference and modality-specific precision are necessary to explain the observed effects.

BayesCurveFit: Enhancing Curve Fitting for Early-Stage Compound Screening Using Bayesian Inference

BayesCurveFit improves dose-response analysis in early drug screening by using Bayesian methods to handle sparse and noisy data, outperforming traditional regression approaches.

Block-Champagne: A novel Bayesian framework for Imaging E/MEG Source

Block-Champagne improves E/MEG source reconstruction by accurately estimating both source locations and extents using a Bayesian framework with block-sparsity constraints and multimodal priors.

Robust single-trial estimates of electrocortical generalized aversive conditioning: Validation of a Bayesian multilevel learning model

The paper validates a Bayesian multilevel learning model for estimating single-trial electrocortical responses during aversive conditioning, showing improved accuracy and handling of missing data.

A flexible Bayesian method for estimating stratigraphic intervals and their co-occurrence in time

This paper introduces a Bayesian method to estimate stratigraphic intervals and their co-occurrence, using empirical examples and a Julia package for implementation.

Design of Protein Sequences with Precisely Tuned Kinetic Properties

The paper designs protein sequences with tailored kinetic properties using IF-MD, demonstrating slower unbinding kinetics in nanobody variants against SARS-CoV-2 RBD.

Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities

This paper introduces a Bayesian method for summarizing phylogenetic tree posteriors using clade probabilities, showing it performs as well or better than existing methods.