Bayesian inference

Incorporation of Epstein-Barr viral variation implicates significance of LMP1 in survival prediction and prognostic subgrouping in Burkitt lymphoma

The paper identifies EBV variants, particularly in LMP1, associated with survival in Burkitt lymphoma and defines prognostic subgroups based on these features.

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

The paper classifies working memory loads using Bayesian functional connectivity and graph convolutional networks, achieving 96% accuracy in intrasubject classification.

Identification and Validation of N-Acetylputrescine in Combination With Non-Canonical Clinical Features As a Parkinsons Disease Biomarker Panel

The paper identifies and validates a biomarker panel combining N-acetylputrescine with clinical features for Parkinson's disease diagnosis, showing high diagnostic accuracy.

Biases in hand perception are driven by somatosensory computations, not a distorted hand model

The paper shows that biases in hand perception arise from Bayesian integration of somatosensory signals, not from a distorted internal hand model.

Dating ammonia-oxidizing bacteria with abundant eukaryotic fossils

This study estimates the origin of ammonia-oxidizing bacteria (AOB) using eukaryotic fossils and Bayesian dating, finding they emerged between 2.1 and 1.9 billion years ago.

Insights into dynamic sound localisation: A direction-dependent comparison between human listeners and a Bayesian model.

This study compares a Bayesian model's predictions of dynamic sound localisation with human performance, showing agreement and insights into how acoustic and sensorimotor information are integrated.

Bayesian Hidden Mark Interaction Model forDetecting Spatially Variable Genes inImaging-Based Spatially ResolvedTranscriptomics Data

The paper introduces a Bayesian model to detect spatially variable genes in imaging-based spatial transcriptomics data, showing high accuracy in identifying genes with novel spatial patterns.

A cross ancestry genetic study of psychiatric disorders from India

This study evaluates the predictive accuracy of polygenic scores for psychiatric disorders in an Indian population using European GWAS data, finding moderate-to-high accuracy for bipolar disorder but lower for OCD and externalizing traits.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

This paper improves variant effect quantification in base editing screens by integrating genotypic and phenotypic data using a Bayesian network called BEAN.

Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models

The paper uses Bayesian priors to address non-identifiability in HMMs applied to patch clamp data, improving inference accuracy with minimal prior assumptions.

A Bayesian Solution to Count the Number of Molecules within a Diffraction Limited Spot

This paper introduces a Bayesian method to estimate the number of fluorescent emitters in diffraction-limited spots, showing improved accuracy over existing methods.

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

This paper introduces the Transmission Tree (TnT) model to improve understanding of epidemic dynamics by integrating pathogen gene sequences and clinical data through a Bayesian approach.

A Bayesian observer model reveals a prior for natural daylights in hue perception

The paper uses a Bayesian observer model to show that hue perception is influenced by a prior preference for blue, aligned with natural daylight variations.

Bayesian Selection of Relaxed-clock Models:Distinguishing Between Independent and Autocorrelated Rates

This study evaluates Bayesian rate model selection in molecular-clock dating, showing convergence to correct models with more data and robustness to minor calibration errors, while introducing an approximation to improve computational efficiency.

Globetrotting geckos: Historical biogeography suggests an Indian origin and an ''Out-Of-India'' dispersal for the cosmopolitan Hemidactylus geckos

This study reconstructs the biogeographic history of Hemidactylus geckos, finding an Indian origin and multiple dispersals from India to Africa and Asia.

MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

Inferring gene regulatory networks by hypergraph variational autoencoder

This paper introduces HyperG-VAE, a Bayesian deep generative model for GRN inference using scRNA-seq data, showing effectiveness in clustering, visualization, and revealing gene regulation patterns.

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases

BayesKAT is a Bayesian framework for genetic association studies that identifies joint genetic effects in complex diseases by adaptively selecting optimal kernels.

Bayesian inference of phylogenetic distances: revisiting the eigenvalue approach

The paper develops a Bayesian method to infer phylogenetic distances incorporating heterotachy and rate variation, finding evidence for a two-domain theory of eukaryotic origins.

Completion of the DrugMatrix Toxicogenomics Database using ToxCompl

The paper predicts missing toxicogenomics data in the DrugMatrix database using ToxiCompl, a hybrid method combining matrix factorization and Bayesian optimization, showing alignment with biological expectations.

FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data

FLIGHTED improves protein fitness model performance by accounting for experimental noise in high-throughput data, showing that data size and architecture matter more than model scale.

Accelerated Bayesian inference of population size history from recombining sequence data

This paper introduces a Bayesian method for inferring population size history from whole genome sequences, showing it is faster and more accurate than existing methods with full uncertainty quantification.

Estimating causality between smoking and abdominal obesity by Mendelian randomization

This study uses Mendelian randomization to estimate the causal effect of smoking on abdominal obesity, finding positive causal effects on waist-hip ratio and visceral fat.

A Model-Based Hierarchical Bayesian Approach to Sholl Analysis

The paper introduces a hierarchical Bayesian model for Sholl analysis to better handle hierarchical data structures in microglia morphology studies.

AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer and loss.

AleRax estimates gene and species trees together using a probabilistic model of gene duplication, transfer, and loss, achieving higher speed and robustness than existing methods.

Large-scale composite hypothesis testing for omics data analyses.

The paper introduces qch_copula, a composite hypothesis testing method for omics data that controls Type I error and detects joint associations, validated on human and plant genetics data.

Robust and Accurate Bayesian Inference of Genome-Wide Genealogies for Large Samples

SINGER improves ARG reconstruction accuracy and uncertainty quantification for large genomic samples by enabling efficient posterior sampling.

Bayesian semantic surprise based on different types of regularities predicts the N400 and P600 brain signals

The paper uses Bayesian models to predict N400 and P600 brain signals by analyzing semantic regularities in sequences of nouns.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia

This paper uses Bayesian inference to estimate parameters of a state feedback control model for vocal pitch in cerebellar ataxia and healthy controls, finding differences in feedback sensitivity and motor response.

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

This paper introduces a Bayesian framework using neural ODEs and pSGLD to discover chemical reaction pathways with uncertainty quantification, showing improved accuracy over standard SGLD.