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This paper uses neural biomarkers to improve the precision and efficacy of vagus nerve stimulation by analyzing inter-subject variability and optimizing dosing procedures.

This paper develops a Bayesian optimization framework for real-time neural stimulation parameter optimization, achieving personalized dosing with fewer stimulations and uncertainty-aware safety.

GPseudoClust jointly infers pseudotime ordering and gene clusters while quantifying uncertainty, showing effectiveness across simulated and experimental datasets.

This paper develops a Bayesian pseudotime method to model transcriptional branching in human embryos and identifies regulators of PGC specification.

GPseudoRank samples from pseudotime order distributions in scRNA-seq data to identify phases of uncertainty and detect cells with distinct biological responses.

This paper introduces branch-recombinant Gaussian processes (B-RGPs) to analyze branching and recombination in biological time series, demonstrating their application to gene expression data in Arabidopsis thaliana.

Clusternomics identifies clusters across heterogeneous datasets by modeling both local and global structures, showing improved performance on simulated and real-world cancer datasets.

The paper compares Bayesian modeling and machine learning for serotype classification of Streptococcus pneumoniae using genomic data, finding machine learning outperforms the Bayesian model with enhanced training data.

This study compares generalised independence criteria for network inference, finding that distance covariance performs well on biological datasets.

gene-cocite identifies and visualizes cocited genes from a list, providing statistical analysis and interactive paper links.