Rob M. Ewing

GAE-Δ uses a graph autoencoder to analyze multi-omics data and predict clinical outcomes while identifying gene network rewiring associated with cancer progression.

This paper identifies 19 ZIKV peptides presented on paediatric brain tumour cells via HLA-I, showing their potential for developing epitope-specific immunotherapies.

This study investigates the immunogenic response of paediatric brain tumour cells to oncolytic Zika virus infection using multiomics approaches, identifying TNF-alpha as a key mediator of oncolytic efficacy and immune activation.

sampleclusteR automates clustering of transcriptomics samples using metadata from GEO and ArrayExpress, correctly grouping most samples across multiple datasets.

This study identifies USP7 as a regulator of cell-cell adhesion in colorectal cancer through its interaction with Ajuba, showing reduced adhesion upon USP7 or Ajuba knockdown.

The paper develops machine learning models using sequence-derived features to predict protein abundance across multiple cell lines and identifies key features for this task.

The paper uses a discrete Morse theory clustering algorithm on a TDA network of gene expression data to identify nine molecular phenotypes associated with asthma and glucocorticoid receptor signaling differences.

iOmicsPASS integrates multi-omics data over biological networks to predict phenotypes and identify predictive subnetworks, improving prediction quality compared to methods without relational information.