Jonathan Gill Lees

This paper introduces eVAE, a discrete-latent model for single-cell perturbation prediction, achieving state-of-the-art results on multiple metrics and outperforming existing methods in perturbation ranking.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

The paper identifies biallelic variants in RNU2-2 as a common cause of recessive neurodevelopmental disorders, showing higher prevalence than the dominant form in England.

This paper introduces Discrete Cell Models (DCM), a diffusion-based framework for single-cell gene expression modeling that operates in the discrete domain, achieving strong performance on benchmark datasets.

The paper identifies recurrent mutations in the RNU2-2P gene, which encodes U2 snRNA, as a cause of a severe neurodevelopmental disorder with prominent epilepsy.

The paper uses phenomics and machine learning to predict protein functions in fission yeast, identifying novel roles and validating predictions through experimental validation.

The paper investigates how insertions in the SARS-CoV-2 Spike N-terminal domain may enhance sialic acid binding and contribute to increased transmissibility.

The paper predicts SARS-CoV-2 spike protein interactions with ACE2 orthologues across 215 vertebrates, finds mutations in ACE2 are more detrimental, and shows human strains are present in animals.

The paper characterizes protein isoforms of the Drosophila sgg gene, revealing distinct localization, interactions, and functions for different proteoforms during development.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

The paper analyzes the structural and functional implications of mutually exclusive splicing across animal genomes, finding that these events are enriched in surface-exposed residues and near functional sites.

This study links polypharmacological drugs to CATH functional families, showing their network properties relate to drug side effects and target identification.