John E. Reid

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

The paper introduces a projection layer to improve deep learning models for regulatory DNA function prediction, showing performance gains on the DeepSEA dataset.

This paper introduces GERAS, a machine learning framework that classifies single-cell transcriptomes into chronological stages with high accuracy and identifies aging-related factors in zebrafish and human pancreatic cells.

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 mutual information estimation methods for transcriptional regulatory network inference using synthetic and real datasets, finding the B-spline estimator performs best and N1/3 bins improve accuracy.