Bayesian inference

BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data

BayesCNet improves inference of cell type-specific regulatory networks by leveraging cell type hierarchy, outperforming existing methods, especially for rare cell types.

Learning genetic values of individuals with incomplete pedigree, genomic and phenotypic data

The paper introduces Hy-BLUP, a Bayesian prediction method for handling incomplete pedigree, genomic, and phenotypic data in genome-enabled prediction.

Microbial dynamics inference at ecosystem-scale

The paper introduces MDSINE2, a Bayesian method for inferring microbial dynamics from time-series data, and shows it outperforms existing methods in identifying ecosystem-scale interactions.

Brain-wide representations of prior information in mouse decision-making

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

A semi-empirical Bayes approach for calibrating weak instrumental bias in sex-specific Mendelian randomization studies

This paper develops a semi-empirical Bayesian framework to calibrate weak instrumental bias in sex-specific Mendelian randomization studies, revealing significant sex differences in causal effects of sleep phenotypes on cardiovascular diseases.

Statistical Causal Discovery in Developing and Refining Adverse Outcome Pathway (AOP)

This paper applies statistical causal discovery to ecotoxicology data to improve adverse outcome pathway models by identifying causal relationships and handling missing data.

MRDadaptis: Self-Adaptive Parameter Configuration Enhances Minimal Residual Disease Detection in Heterogeneous ctDNA Samples

MRDadaptis improves MRD detection in heterogeneous ctDNA samples by automatically adapting parameters using Bayesian optimization and meta-learning, enhancing detection stability and performance.

A Bayesian Framework for Genome-wide Circadian Rhythmicity Biomarker Detection

This paper introduces a Bayesian framework for detecting circadian rhythmicity biomarkers across the genome, showing improved false discovery rate control and performance in RNA-sequencing data.

Bayesian estimation yields anti-Weber variability

The paper examines Bayesian observer variability in a numerosity-estimation task and finds anti-Weber behavior, similar to human subjects, suggesting variability is influenced by prior distribution skewness.

Precise temporal localisation of M/EEG effects with Bayesian generalised additive multilevel models

This paper introduces a Bayesian generalised additive multilevel model for precise temporal localization of M/EEG effects, showing improved accuracy in estimating onset and offset compared to conventional methods.

EVOLUTIONARY ASSEMBLY OF CROWN REPTILE ANATOMY CLARIFIED BY LATE PALEOZOIC RELATIVES OF NEODIAPSIDA

Synchrotron microtomography and a 167-taxon phylogenetic matrix place Millerettidae as sister to Neodiapsida (forming Parapleurota), dismantling Parareptilia and Eureptilia while dating tympanic hearing to the middle Permian.

Bayesian phylodynamics for developmental biology: incorporating age-dependence

The paper extends phylodynamic models to account for age-dependent cell division in developmental biology, showing biases in previous estimates using birth-death models and providing a BEAST2 implementation.

Hierarchical Bayesian Modelling of Interoceptive Psychophysics

This paper introduces hierarchical Bayesian models for cardiac and respiratory interoceptive psychophysics to estimate sensitivity, bias, and precision with fewer trials or participants.

Comparative Computational Modeling of Approach-Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference

This study compares computational models of approach-avoidance biases in suicidal populations using hierarchical Bayesian inference, finding that simpler models with value decay better explain behavior than complex ones.

The Occurrence Birth-Death Diffusion Process: Unraveling Diversification Histories with Fossils and Heterogeneous Rates

The paper introduces the Occurrence Birth-Death Diffusion process to integrate fossil data into diversification analyses, showing fossils are essential for accurate diversification rate and richness trajectory inference.

Identifying genomic adaptation to local climate using a mechanistic evolutionary model

The paper develops a hierarchical Bayesian model to identify local adaptation by accounting for observational uncertainty and evolutionary processes, finding 30 genes associated with temperature seasonality in Willow Flycatchers.

INDITEK-2.0: A Bayesian inverse eco-evolutionary modelling framework for reconstructing Phanerozoic biodiversity

INDITEK-2.0 uses Bayesian inverse modelling to estimate eco-evolutionary parameters from synthetic biodiversity data, successfully recovering true parameter values with low uncertainty.

Let there be multifunctionality: Uncovering the criticality zoo of the AC-DCgenetic circuit

The paper analyzes the AC-DC genetic circuit to show it can exhibit diverse multifunctional dynamics, including coexisting oscillations and multistability, challenging traditional views of network constraints.

Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting

This paper introduces a Bayesian nonparametric framework for analyzing single-molecule fluorescence data, demonstrating its effectiveness through synthetic and experimental data.

Executive control in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium

This study analyzes fMRI data from OCD patients and controls to identify neural activation patterns during executive control tasks, finding reduced activation in frontoparietal regions and increased default mode network activity in OCD.

Context-dependent structurally informed effective connectivity under psilocybin

This study examines how structural connectivity shapes brain dynamics under psilocybin using effective connectivity models and fMRI data across different contexts.

Dynamic Inference by Model Reduction

This study proposes an approach based on active inference with dynamic priors to infer dynamic intentions and perform tasks like trajectory prediction and object grasping.

Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune suppressive parameters in metastatic breast cancer

The paper develops mathematical models and Bayesian inference methods to analyze tumor-immune dynamics in metastatic breast cancer, revealing immune suppressive parameters as key predictors of response to immunotherapy.

Increasing certainty in systems biology models using Bayesian multimodel inference

This paper uses Bayesian multimodel inference to improve predictive certainty in systems biology models of intracellular signaling pathways.

Reduced amino acid substitution matrices find traces of ancient coding alphabets in modern day proteins

The paper develops substitution models that account for changes in the coding alphabet size over time, finding support for a reduced ancient alphabet in old proteins and rejecting it in young ones.

GPFN: Prior-Data Fitted Networks for Genomic Prediction

This paper introduces GPFN, a new genomic prediction method that outperforms linear models on multiple traits and matches their performance on a structured prediction task.

Highly multiplexed spectral FLIM via physics informed data analysis

This paper introduces a Bayesian S-FLIM framework that learns spectra and lifetimes photon-by-photon, enabling efficient deconvolution of up to 9 fluorophore species with overlapping spectra.

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions

Cosmos is a Bayesian framework that identifies direct and indirect effects of protein variants in multi-phenotype DMS data by inferring causal relationships and enabling counterfactual interpretation.

Cross-sectional, interventional, and causal investigation of insulin sensitivity using plasma proteomics in diverse populations

This study identifies plasma proteins associated with insulin sensitivity across diverse populations and shows their response to interventions, with some proteins suggesting causal relationships and therapeutic targets.

Genetically determined platelet traits impact stroke risk through multiple mechanisms and cell types

This study identifies platelet traits as key risk factors for stroke using Mendelian Randomization and subclustering, revealing mechanisms involving platelet reactivity and endothelial cell adhesion.