Bayesian inference

Efficient Optimization of Genotype Pairs for Intercropping using Genomic Prediction and Bayesian Optimization

This paper develops a framework combining genomic prediction and Bayesian optimization to efficiently identify high-performing genotype pairs for intercropping.

TreeGazer: Prospecting Protein Sequence-Function Landscapes via Phylogenetic Structure

TreeGazer selects informative protein sequences using phylogenetic structure and Bayesian optimization, outperforming alternative strategies in simulated campaigns and low-data settings.

A modular Bayesian framework for inferring transmission networks from polyclonal infections, with application to Plasmodium falciparum

This paper introduces a Bayesian framework for inferring transmission networks from polyclonal infections, demonstrating its application to Plasmodium falciparum with high precision and recall in simulations.

MM-ComBat and MM-CovBat: Multivariate Frameworks for Joint Harmonization of Multi-Metric Neuroimaging Data

The paper introduces MM-ComBat and MM-CovBat, multivariate frameworks to harmonize multi-metric neuroimaging data by jointly addressing batch effects across metrics and features.

Efficient Bayesian inference for ordinary differential equation models from experimental data with uncertain measurement times

This paper presents a Bayesian framework for parameter inference in ODE models that accounts for uncertain measurement times, improving reliability and efficiency in dynamic modeling.

A generative artificial intelligence approach for antibiotic optimization

This paper introduces APEXGO, a generative AI framework for optimizing antimicrobial peptides, achieving high success rates in enhancing antimicrobial activity and outperforming existing methods.

Beyond Redfield: Thermodynamic Bounds and Non-Perturbative Quantum Dynamics in Tubulin Networks

This paper quantifies quantum coherence in tubulin networks, showing that functional relevance requires amplification beyond thermodynamic limits and introduces a framework to evaluate biological quantum-coherence claims.

The Contribution of the Koniocellular Visual Pathway to Aversive Learning in Human Visual Cortex

This study investigates how the koniocellular visual pathway contributes to aversive learning by comparing ssVEP responses to threat and safety cues using tritan and luminance stimuli.

Cryo-electron microscopy ensemble optimization using individual particles and physical constraints

This paper introduces a Bayesian optimization method for cryo-EM ensemble optimization, directly inferring structures and population weights from particle images and physical constraints.

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data

This paper introduces a hierarchical Bayesian modelling approach for diffusion and exchange MRI data, showing improved accuracy and noise resilience compared to traditional least-squares methods.

A Differentiable dFBA Simulator for Scalable Bayesian Inference over Microbial Metabolic Models

This paper introduces a differentiable dFBA simulator for Bayesian inference over microbial metabolic models using OD600 measurements, enabling scalable analysis of fermentation data.

Measurement of atomic scattering factors by cryo-electron microscopy

This paper introduces a Bayesian method to estimate atomic scattering factors from cryo-EM data, improving accuracy by incorporating molecular environment effects.

Conjugation structures plasmid populations through host-lineage restriction

The paper quantifies host-lineage restriction of plasmid backbones in E. coli isolates and shows how this restriction structures plasmid co-occurrence.

PHYFUM: Phylogenetic Reconstruction of Normal and Pre-malignant Tissue Evolution Using Fluctuating Methylation

PHYFUM reconstructs the evolutionary history of normal and pre-malignant tissues using methylation data, revealing early gland divergence in the gut and later divergence in the endometrium.

Novel linkage disequilibrium-based genotype-by-environmental interaction method for genomic prediction of cotton yield and fibre quality traits

This paper develops a GEI-based genomic prediction method for cotton traits using dimensionality reduction and Bayesian models, showing better performance than deep learning approaches.

BiGER: Bayesian Rank Aggregation in Genomics with Extended Ranking Schemes

BiGER is a Bayesian rank aggregation method for meta-analysis of gene lists, handling mixed rankings and missing data with high accuracy.

Evidence of Epistatic Interactive Effects of HK1 and GCK Genes on Circulating Hemoglobin A1c Levels

The paper identifies epistatic interactions between HK1 and GCK genes affecting HbA1c levels using Bayesian methods and confirms findings in independent studies.

Mitigating Family Effects in RNA Secondary-Structure Prediction with Latent-Space Continual Learning

The paper introduces RNAFOLBO, a continual learning framework that improves RNA secondary-structure prediction by mitigating family effects and catastrophic forgetting.

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

EnzCast predicts enzymatic kinetics from multi-modal data, and IsoKin infers patient-specific in vivo kinetics, showing improved uncertainty reduction in colorectal cancer.

Marine biotoxin depuration rates: management applications, research priorities, and predictions for unstudied species

This paper reviews marine biotoxin depuration rates, identifies knowledge gaps, and predicts depuration rates for unstudied species using a Bayesian model.

Single cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease

The paper fine-maps PD GWAS signals across cell types, revealing convergent glial-neuronal risk genes and a shared vesiculopathy network.

A Bayesian approach for identifying similar transcript dynamics using curve registration

This paper introduces a Bayesian method to align transcript dynamics across different time scales and species, enabling comparative analysis of gene expression patterns.

AutoLead: An LLM-Guided Bayesian Optimization Framework for Multi-Objective Lead Optimization

AutoLead uses LLMs and Bayesian optimization to optimize drug-like molecules for multiple objectives, achieving state-of-the-art results on a new benchmark dataset.

Semi-parametric Empirical Bayes Method for Multiplet Detection in snATAC-seq with Probabilistic Multi-omic Integration

SEBULA detects multiplets in snATAC-seq using a semi-parametric empirical Bayes model and integrates with scRNA-seq data, showing superior performance on benchmark datasets.

Parallel algorithms for phylogenetic inference under a structured coalescent approximation

The paper introduces parallel algorithms to improve the computational efficiency of structured coalescent models for phylogeographic analysis, enabling faster and larger-scale studies of pathogen spread.

Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control

The paper investigates nonergodicity and Simpson's paradox in cognitive control dynamics using brain imaging and behavioral data from over 4,000 children, revealing brain-behavior associations that reverse between between-subjects and within-subjects analyses.

AutoNeuro: An Open-Source fMRI Toolbox for Real-Time Neuroadaptive Task Design

AutoNeuro enables real-time fMRI with adaptive task design by integrating acquisition, preprocessing, and Bayesian optimization for closed-loop neuroimaging.

Identity-state expression and switching in co-conscious dissociative identity disorder: a non-traumatic proof-of-concept fMRI study

This study uses fMRI to investigate neural differences between identity-states in DID without trauma provocation, finding distinct brain activity patterns during state switching.

Anchored Brownian motion and Bayesian methods for the analysis of single particle tracking data

The paper introduces a Bayesian method based on anchored Brownian motion to estimate diffusion coefficients from single particle tracking data, showing improved performance over conventional methods.

Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping

The paper introduces CASE, a Bayesian framework for cell-type-specific and shared eQTL fine-mapping, showing improved performance over existing methods in simulations and real data.