Bayesian inference

A causal model of human growth and its estimation using temporally-sparse data

The paper develops a causal model of human growth using Bayesian statistics to compare metabolic and allometric factors across populations and simulate healthcare interventions.

Distinct metabolic signatures of Alzheimer's and Parkinson's disease revealed through genetic overlap with metabolic markers

This study identifies distinct genetic overlaps between Alzheimer's and Parkinson's disease and metabolic markers, revealing disease-specific pathways involving lipid metabolism and synaptic processes.

Genomic and molecular evidence that the lncRNA DSP-AS1 modulates Desmoplakin expression

The paper identifies a variant in DSP associated with QRS interval and shows that DSP-AS1 regulates DSP expression through functional and Mendelian randomization analyses.

A Bayesian Hierarchical Model of Trial-to-Trial Fluctuations in Decision Criterion

This paper introduces hMFC, a Bayesian model to estimate slow decision criterion fluctuations from limited data, showing it improves accuracy over stable criterion assumptions.

Extracting Value Coding Features from Individual Serotonin Neurons

The paper analyzes serotonergic neuron activity during a dynamic Pavlovian task to estimate reward history and its relation to behavior.

HBMAP: Bayesian inference of neural circuits from DNAbarcoded projection mapping

HBMAP infers neural circuit projections from DNA-barcoded data using Bayesian clustering, accurately identifying patterns and accounting for subject variability.

Seed dispersal disruption limits tropical forest regrowth

In field records from 3026 tropical sites, modeled bird and mammal seed dispersal disruption predicts slower natural-regrowth carbon accumulation, an estimated 57% average loss at candidate restoration sites.

Enhancing Pharmacometric Modeling with Full Bayesian Inference and Students t-Based M3 Censoring: A Simulated Population PK Study on Robust Handling of Outliers and Censored Data

This study evaluates Bayesian modeling strategies for population pharmacokinetics, finding that Students t-based M3 censoring improves accuracy and precision in handling outliers and censored data.

Comparative analysis of MS/MS search algorithms in label-free shotgun proteomics for monitoring host-cell proteins using trapped ion mobility and ddaPASEF

This study benchmarks six MS/MS search algorithms for label-free shotgun proteomics of host-cell proteins using trapped ion mobility and ddaPASEF, identifying performance differences in sensitivity, accuracy, and bias.

Procrastination partly reflects an evolutionary byproduct of non-planning impulsivity

The paper shows non-planning impulsivity predicts procrastination and shares genetic overlap, with DLPFC deviations partially explaining this link.

Future shapes present: autonomous goal-directed and sensory-focused mode switching in a Bayesian allostatic network model

This paper introduces a Bayesian recurrent neural network model that autonomously switches between goal-directed and sensory-focused behaviors to adapt to environmental changes.

MICRORNAS AS REGULATORS OF DRUG METABOLISM AND TRANSPORT IN PREGNANT AND LACTATING WOMEN

This study identifies differentially expressed miRNAs regulating drug metabolizing enzymes and transporters during pregnancy and lactation, revealing their association with sex hormone levels and placental function.

The Fossilized Birth Death Process with heterogeneous diversification rates unravels the link between diversification and specialisation to a carnivorous diet in Nimravidae (Carnivoraformes)

This paper introduces F-ClaDS, a new diversification model for phylogenetic inference, and applies it to Nimravidae fossils to show hypercarnivory drives speciation but also increases extinction risk.

Robust and scalable inference of cancer progression pathways using Conjunctive Bayesian Networks

This study introduces R-CBN, a robust Bayesian network model for inferring cancer progression pathways, showing improved reliability and scalability over existing methods.

TabSyM: A Generative Pipeline for Small Multi-Cohort Omics Tabular Data

TabSyM improves predictive modeling of small, high-dimensional biomedical datasets by generating synthetic data and aligning cross-cohort distributions.

Walking the Tightrope: Balancing Opposing Cooperativities as an Operating Principle in Dynein Assembly.

This paper characterizes a seven-state binding network in dynein assembly using 39 ITC experiments and Bayesian inference, revealing previously unrecognized binding states and balancing cooperativities.

Perceptual clustering in auditory streaming

This paper introduces a Bayesian inference model for perceptual clustering in auditory streaming, demonstrating its ability to explain experimental data and make novel predictions.

Memory-based incremental parameter updating of a generic stochastic plant epidemic model

This paper introduces a sequential Monte Carlo method for real-time parameter updating in a stochastic plant epidemic model, showing accurate convergence and forecasting capabilities.

Locomotor Trends Among Early Mammals Illuminated by Predictors of Arboreality from the Mammalian Appendicular Skeleton

Bayesian multilevel models of 69 appendicular skeletal metrics across 236 extant mammal species predict climbing at 95% accuracy and indicate that several Mesozoic therians, including Juramaia, were terrestrial ground-dwellers.

Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry

This paper improves Bayesian parameter inference for ITC by resolving prior ambiguity through multi-dataset analysis and hierarchical modeling, enhancing accuracy in binding parameter estimation.

Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

This paper introduces a Bayesian model to infer intra-tumoral clonal structure and copy number alterations from scRNA-seq data, achieving high accuracy in clustering and CNA state inference.

Natural gradient Bayesian sampling automatically emerges in canonical cortical circuits

The paper shows canonical cortical circuits naturally implement advanced Bayesian sampling algorithms, including natural gradient methods, through nonlinear dynamics and interneuron control.

Functionally-structured Bayesian model for localizing neural activity and information in magnetoencephalography signals

This paper introduces a functionally-structured Bayesian model to improve MEG source estimation accuracy and reduce information spreading.

Improving Joint Estimation of Vital Rates in IPMs via Gaussian Processes and ABC

This paper introduces ABC GP IPM, a flexible IPM framework using Gaussian Processes and Approximate Bayesian Computation to improve demographic modeling across species.

ABC for high-dimensional modular models via MCMC samples

The paper introduces an improved ABC-PMC method for high-dimensional modular models by leveraging precomputed MCMC samples and adaptive summary statistics, significantly reducing computational cost.

Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback

This study uses Bayesian optimization to find optimal stimulation patterns for cortical neuroprosthetics, improving phosphene perception quality through patient feedback.

The Refined Recurrence Risk of De Novo variants Due to Parental Mosaicism

The paper assesses parental mosaicism in families with de novo variants, finding 4.4% parental mosaicism and proposing amplicon-based sequencing for improved recurrence risk assessment.

Accelerating Cell Culture Media Development Using Bayesian Optimization-Based Iterative Experimental Design

This paper uses Bayesian optimization to accelerate cell culture media development, achieving better results with fewer experiments in two applications.

Emergence of zoonotic and multi-drug resistant Streptococcus suis

This study identifies the emergence and evolution of two zoonotic Streptococcus suis lineages in Thailand, showing they acquired multi-drug resistance and serotype 2 capsule through horizontal gene transfer.

Co-fluctuations of genes in single cells predict transcriptome-wide outcomes to perturbations

CIPHER uses gene co-fluctuations in unperturbed cells to predict transcriptome-wide responses to perturbations, outperforming existing methods and revealing key regulatory structures.