Bayesian inference

From field naturalism to Bayesian models: fog-frost interaction shapes growth form partitioning along Himalayan gradient

The paper develops a Bayesian model to analyze growth form partitioning along a Himalayan gradient, finding that fog-frost interactions are more influential than altitude in shaping vegetation patterns.

Improving the efficiency of single-driver ecological experiments with Bayesian optimal experimental design

This paper develops Bayesian optimal experimental designs to improve efficiency in ecological experiments by reducing the number of measurements needed for accurate parameter estimation.

Inference of fitness landscapes with heterogeneous patterns of epistasis across sites

This paper introduces a framework to infer fitness landscapes with structured epistasis and applies it to protein and RNA mutagenesis data, revealing complex genetic interactions.

Active Learning-Guided Peptide Design for Modulating Condensate Properties upon Recruitment

The paper uses active learning to design peptides that modulate the physical properties of a biomolecular condensate by interacting with MUT-16.

SimMapNet: A Bayesian Framework for Gene Regulatory Network Inference Using Gene Ontology Similarities as External Hint

SimMapNet improves gene regulatory network inference by integrating Gene Ontology similarities, outperforming existing methods on bacterial and fly datasets.

Bayesian Nonparametric Identification of Frequency-Selective Neural Oscillatory States

This paper introduces a Bayesian nonparametric method to identify frequency-selective neural oscillatory states without predefining frequency bands or fixing the number of states.

Multivariate Bayesian Inversion for Classification and Regression

This paper introduces multivariate Bayesian inversion as an alternative to machine learning for supervised tasks, demonstrating improved interpretability and probabilistic predictions.

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

This paper shows that Cauchy perturbation kernels outperform Normal kernels in ABC-SMC by addressing overinflation and overconcentration issues, improving performance in high dimensions.

DextraDemixer enables accurate identification of antigen-specific T cells from pMHC multimer experiments

DextraDemixer improves identification of antigen-specific T cells from pMHC multimer data by using a Bayesian model and dynamic thresholding to reduce false discoveries.

Inferring antifungal drug synergy from Candidozyma auris optical density data using Bayesian mechanistic modelling

This paper develops a Bayesian mechanistic model to infer antifungal drug synergy and drug-action kinetics from optical density data of Candidozyma auris.

A blinded, counterbalanced rater design for evaluating AI-assisted summarisation of tertiary clinical genomics reports: methodology of the QNOMX-VHIR-CPSP-001 Phase 1 study

This paper describes a blinded, counterbalanced study design to evaluate AI-assisted summarisation of clinical genomics reports using a quality rubric and statistical models.

Disentangling adiposity-related and non-adiposity-related genetic pathways for type 2 diabetes

This paper identifies 29 circulating proteins associated with type 2 diabetes risk independent of BMI using GWAS-by-subtraction, proteome-wide Mendelian randomization, and colocalization analyses.

Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients

This paper introduces a Bayesian framework for differential gene expression analysis that scales to over 10 million cells and integrates data from multiple patients.

BAYESIAN STATE-SPACE MODEL FOR JOINT INFERENCE OF OSCILLATORY DYNAMICS AND POINT-PROCESS COUPLING

This paper introduces a Bayesian state-space model to jointly infer oscillatory dynamics and spike-field coupling, showing improved frequency-specific estimates compared to classical methods.

Bayesian-enhanced closed-loop optimization of ultrasound protocols for targeted and precise neuromodulation

This paper develops BEACUN, a Bayesian-enhanced adaptive control method for optimizing LIFU neuromodulation protocols in rats, achieving faster convergence than conventional methods.

Investigating the Y chromosome in complex disease: Phenome-wide scan across 104,334 Finnish men

Phenome-wide scan of Y-chromosomal variants against 1,426 traits in 104,334 Finnish men finds no significant hits; kin-based validation supports haplogroup I1 increasing coronary heart disease risk.

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues

The paper identifies a widespread transcript leakage issue in spatial transcriptomics and introduces DeLeakage, a Bayesian method that improves data cleaning and cell-type annotation.

A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders

This paper introduces FGMB, a multi-context regulome-wide association atlas, and applies it to Alzheimer's disease to identify 327 regulatory associations and resolve 86 gene-molecular-trait pairs.

Unraveling the Genetic Overlap Between Parkinson's Disease and Schizophrenia Through Genome-wide Association and Cell-Type Specific Transcriptomic Analysis

This study identifies shared genetic loci between Parkinson's disease and schizophrenia using GWAS and scRNA-seq, highlighting RAI1's role in mitochondrial dysfunction and non-motor symptoms.

Temporally overlapping mechanisms diversify clonal B cell responses in vivo.

Single-cell RNA and BCR sequencing of infected mouse spleens shows class switching overlaps clonal expansion, GC-seeding clones also form plasmablasts, and SHM proceeds at constant rate.

Relational biological structure improves fine-mapping of causal GWAS variants under weak signal

This paper introduces GAFM and HBP methods to improve GWAS fine-mapping by leveraging biological structure, achieving higher accuracy and speed across multiple species and datasets.

OrganScaleR: An Open Shiny Tool for Principled Organ-Weight Inference in Mouse Physiology

OrganScaleR provides a Shiny tool for size-adjusted organ-weight analysis in mice, avoiding misleading ratios by using ANCOVA or causal mediation based on body-weight overlap.

MIDFA: Scalable Bayesian Factor Analysis for Mixed and Incomplete Data

The paper introduces a scalable Bayesian factor analysis framework for handling mixed and incomplete data, demonstrating its effectiveness on MS datasets.

A global map for introgressed structural variation and selection in humans

Long-read phased assemblies from two Papuans plus 47 HPRC genomes map archaic introgressed structural variants, revealing 11 candidate archaic centromeres and 16 candidate adaptive SVs.

Robust discovery of mutational signatures using power posteriors

This paper introduces a Bayesian NMF approach with power posteriors and sparsity-inducing priors to improve mutational signature discovery, showing better accuracy than existing methods.

Sciphy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.

SciPhy estimates time-scaled phylogenies and cell population parameters from sequential genetic lineage tracing data using a Bayesian framework, showing improved accuracy over existing methods.

The Conductome: A Bayesian Classifier Approach to Predicting and Understanding Behaviour

The paper introduces a Bayesian classifier framework to predict behaviour by operationalizing it as a Conductome, demonstrating its effectiveness on a dataset predicting sedentariness.

HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling

HSSM provides a Python toolbox for hierarchical Bayesian neuro-cognitive modeling, enabling fast parameter estimation and model simulation through simulation-based 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.