Bayesian inference

Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability

This study develops a computational framework combining biophysics and machine learning to predict cardiac growth probability, validated using canine MVR data.

Discovering uncertainty: Bayesian constitutive artificial neural networks

This paper introduces Bayesian constitutive neural networks to model soft matter systems, showing they can discover interpretable models, quantify uncertainties, and distinguish between healthy and diseased arteries.

Dynamic Causal Modeling in Probabilistic Programming Languages

This paper develops Bayesian inference methods for Dynamic Causal Modelling using probabilistic programming languages to improve neural dynamics prediction.

Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal

The paper evaluates how model and prior choices affect the Bayesian temporal signal detection method BETS and provides guidelines for reliable molecular clock estimates.

Unified Analysis of Multi-order Tensors for Integrative Molecular Profiling

The paper introduces MUSIC, a Bayesian tensor decomposition framework for integrating multi-modal molecular data, showing it reveals interpretable patterns across patients, cell types, and modalities.

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

This study evaluates Bayesian Linear Regression models for gene set prioritization in complex diseases, showing improved performance in identifying T2D-related pathways using multi-trait analysis.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

The paper develops a multimodal AI/ML framework to identify biomarkers and predict cardiovascular disease using multi-omics data, finding a 27-component signature with high predictive accuracy.

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

MSI-SEER uses deep Gaussian processes to predict microsatellite instability from histology images, achieving high accuracy in MSI prediction and ICI response prediction.

The Bayesian Phylogenetic Bootstrap, Application to Short Trees and Branches

The paper introduces a Bayesian phylogenetic bootstrap method to improve branch support interpretation in phylogenetics, showing higher expected support for correct branches compared to the frequentist Felsenstein bootstrap.

Quantifying tumor specificity using Bayesian probabilistic modeling for drug target discovery and prioritization

This paper introduces BayesTS, a Bayesian probabilistic score for tumor specificity to prioritize drug targets, demonstrating its effectiveness across multiple tissues and data types.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

This paper introduces a statistical RNA velocity model that improves dynamical consistency and inference of cell cycle speed from scRNA-seq data.

Analysis of sparse animal social networks

The paper addresses bias in estimating edge weights in sparse animal social networks by introducing zero-inflation into a Bayesian framework, demonstrating improved performance over frequentist methods in elephant data.

Statistical curve models for inferring 3D chromatin architecture

The paper improves a method for inferring 3D chromatin structure from Hi-C data by introducing a new framework that addresses initialization issues and extends distribution models for contact counts.

BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data

BTS identifies causal variants and their functional contexts using GWAS data and functional genomic annotations, showing improved efficiency and biological insights.

ENKIE: A package for predicting enzyme kinetic parameter values and their uncertainties

ENKIE predicts KM and kcat enzyme kinetic parameters and their uncertainties using Bayesian Multilevel Models, achieving performance comparable to deep learning methods.

How relevant is the prior? Bayesian causal inference for dynamic perception in volatile environments

The paper evaluates Bayesian causal inference for perceptual decisions in volatile environments, finding that participants integrate priors when prediction errors are small but reduce reliance as errors increase, indicating prior irrelevance.

Bayesian modeling of mutual exclusivity in cancer mutations

The paper develops a Bayesian framework to model mutual exclusivity in cancer mutations, finding potential mutual exclusivity between EGFR and IDH1 in glioblastoma multiforme.

Rationalised experiment design for parameter estimation with sensitivity clustering

This paper introduces PARSEC, a model-based design of experiments framework that improves parameter estimation by using parameter sensitivity clustering and Bayesian computation.

Classical statistical methods are powerful for the identification of novel targets for the survival of breast cancer patients

This study uses classical statistical methods to identify survival-related genes in breast cancer, finding two novel targets and validating results through bootstrapping.

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine

The paper identifies expression-driven genetic dependency targets for precision oncology using a Bayesian approach, revealing new druggable genes and validating their therapeutic potential.

Bayesian inference in arm posture perception

The paper tests a Bayesian model of arm posture perception using a VR task, finding that a Gaussian prior better explains individual biases than a uniform prior.

KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks

KinPFN approximates RNA folding kinetics using prior-data fitted networks, achieving fast and accurate predictions with synthetic data training.

Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease

This study uses CRISPR perturbations in CD4+ T cells to infer a gene regulatory network, revealing trans-regulatory mechanisms linking immune GWAS genes to signaling pathways.

Bayesian parameter inference for epithelial mechanics

This study improves parameter estimation in epithelial mechanics models using Bayesian statistics, showing accurate results on synthetic data and revealing the dispensability of cortical elasticity in vivo.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

This paper unifies statistical genetics and phylogenetics by developing a general model for genetic covariance, showing how GWAS and phylogenetic regression are special cases and demonstrating improved association testing through shared methodology.

Modelling Microbiome Association with Host Phenotypes Using a Bayesian Dirichlet Process Model

The paper introduces a Bayesian Dirichlet process model to analyze microbiome associations with host phenotypes, showing high statistical power in simulated and real datasets.

Inferring post-transcriptional regulation within and across cell types in human testis

The paper quantifies proteomes and RNA levels in human testis cells to infer post-transcriptional regulation using a Bayesian model called BayesPG.

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis

The paper introduces SpatialTopic, a Bayesian topic model for multiplexed imaging analysis that identifies spatial features and tracks their dynamics across disease progression.

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA

The paper introduces an infinite GPFA model to infer dynamically changing latent factors in neural data, showing it captures meaningful dynamics in hippocampal place cells during spatial tasks.

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

PenguiN improves strain-resolved assembly of viral genomes and bacterial 16S rRNA from metagenomics by using overlap-based methods and a Bayesian model, resulting in longer contigs and more complete genomes.