Bayesian inference

Modelling the demographic history of human North African genomes points to a recent soft split divergence between populations

The paper models the demographic history of North African genomes, finding a recent soft split divergence between Arab and Amazigh populations and a back-to-Africa origin.

Inferring causal connectivity from pairwise recordings and optogenetics

The paper demonstrates how causal inference methods like instrumental variables and difference in differences can reduce confounding in optogenetic experiments to estimate effective neural connectivity.

Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization

This paper proposes a risk-aware antibody design framework using protein language models and Bayesian optimization, showing improved efficiency and sequence quality over existing methods.

Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

The paper shows that recurrent neural circuits with Poisson spiking variability perform optimal Bayesian inference by sampling from posterior stimulus distributions.

Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

This paper introduces a Bayesian multilevel model to estimate prevalence dynamics from pooled and individual wildlife samples, accounting for dilution effects and false negatives.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

CONGAS+ is a Bayesian method that maps single-cell RNA and ATAC data to copy number clones, revealing subclonal architectures and genotype-phenotype relationships in cancer.

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

FitMultiCell provides a scalable pipeline for modeling, simulating, and parameterizing multi-scale multi-cellular processes using Morpheus and pyABC.

SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

SCsnvcna places SNVs on a CNA tree using independent cell sets, improving accuracy in predicting SNV placement and losses due to copy number changes.

From average transient transporter currents to microscopic mechanism -- A Bayesian analysis

This paper uses Bayesian analysis to evaluate the ability of SSME data to identify kinetic parameters and mechanisms of secondary active transporters.

Bayesian Fluorescence Framework for integrative modeling of biomolecules

The paper introduces BFF, a Bayesian framework for processing fluorescence data to improve biomolecular modeling accuracy and precision using synthetic single-molecule FRET data.

Genotype grouping and bio-geographical analysis revealed the worldwide spatiotemporal spread of Citrus yellow vein clearing virus

This study uses genome sequencing and phylogenetic analysis to trace the origin and spread of Citrus yellow vein clearing virus, identifying South Asia as its likely origin and highlighting a recent divergence in California isolates.

Asymptotically exact fit for linear mixed model

This paper introduces IDUL and IDUL{dagger}, efficient and asymptotically exact methods for fitting linear mixed models in genetic association studies, particularly for multiomics data.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Rosace improves analysis of deep mutational scanning data by using Bayesian shrinkage and position information, outperforming existing methods in simulations and real data.

A generalised method for experiment design and model selection in the Bayesian framework

This paper introduces a Bayesian Optimal Experiment Design method for parameter estimation and model selection, demonstrating its effectiveness in systems biology models.

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro

The paper identifies and removes background noise in CITE-seq data using DecontPro, a Bayesian model that improves clustering specificity and reduces contamination from spongelets and ambient ADTs.

Understanding and Predicting Polycystic Ovary Syndrome through Shared Genetics with Testosterone, SHBG, and Chronic Inflammation

This study identifies genetic loci associated with PCOS and shows genetic correlations with inflammation and sex hormones, improving PCOS prediction through polygenic risk scores.

Bayesian inference of relative fitness on high-throughput pooled competition assays

This paper introduces a Bayesian framework to infer microbial fitness from lineage tracking assays, accounting for noise and scaling to large datasets.

Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)

This paper develops an ABC framework to estimate state-dependent diversification rates in the BiSSE model and evaluates its performance against likelihood-based methods.

Identifying summary statistics for approximate Bayesian computation in a phylogenetic island biogeography model

This study proposes summary statistics for approximate Bayesian computation in a phylogenetic island biogeography model and evaluates their performance against likelihood-based methods.

Probabilistic Graphical Modeling under Heterogeneity

This paper introduces GraphR, a Bayesian method for probabilistic graphical modeling under sample heterogeneity, showing improved performance in network estimation and computational efficiency.

Signalling pathway crosstalk stimulated by L-proline drives differentiation of mouse embryonic stem cells to primitive-ectoderm-like cells

The paper shows that L-proline drives mouse embryonic stem cells to become primitive-ectoderm-like cells through signaling pathway interactions.

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data

This paper introduces a Bayesian model to infer transcription factor activity from gene expression data using logical rules and applies it to bulk and single-cell transcriptomics to study fibroblast plasticity.

TreePPL: A Universal Probabilistic Programming Language for Phylogenetics

This paper introduces TreePPL, a probabilistic programming language for phylogenetics, enabling flexible modeling and inference with various algorithms.

Baldur: Bayesian hierarchical modeling for label-free proteomics exploiting gamma dependent mean-variance trends

The paper introduces a Bayesian hierarchical model for label-free proteomics that improves detection of true positives while reducing false positives compared to existing methods.

Deep resequencing of the 1q22 locus in non-lobar intracerebral hemorrhage

The paper performs deep resequencing of the 1q22 locus in non-lobar ICH cases and controls to identify causal variants and mechanisms.

PAST: latent feature extraction with a Prior-based self-Attention framework for Spatial Transcriptomics

PAST characterizes spatial domains in spatial transcriptomics data using a variational graph convolutional auto-encoder with prior-based self-attention, improving downstream analyses.

Incorporating extrinsic noise into mechanistic modelling of single-cell transcriptomics

The paper introduces a Bayesian framework to model single-cell RNA sequencing data, accounting for extrinsic noise and cellular heterogeneity, showing cell-specific effects alter gene expression dynamics.

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

The paper develops a physically constrained recurrent neural network and Bayesian experimental design framework to optimize microbial community functions by efficiently navigating complex design spaces.

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells

This paper introduces ODeGP, a Gaussian Process-based method for detecting oscillations in time series, and shows it outperforms existing methods in detecting weak and non-stationary rhythms in qPCR data.

Posterior marginalization accelerates Bayesian inference for dynamical systems

This paper introduces a method to accelerate Bayesian inference for dynamical systems by marginalizing the posterior distribution, improving computational efficiency by up to 50 times in effective sample size.