Laura‐Jayne Gardiner

The paper predicts cancer treatment response using a MIL-attention framework that analyzes macrophage states from single-cell RNA data, showing improved performance over existing methods.

This paper uses RNA foundation models to improve endometriosis classification across cohorts and identifies stable gene-level predictors.

This paper uses machine learning to predict circadian gene expression in Arabidopsis using DNA sequence features, identifies key regulatory elements, and optimizes sampling strategies for circadian transcript prediction.

The paper develops an explainable AI approach to predict skin-related phenotypes from leg skin microbiome data and identifies microbial composition changes linked to these traits.

The paper maps the Yr7 yellow rust resistance gene in wheat using QTL mapping, association mapping, and mapping-by-sequencing, narrowing the interval to a candidate gene.

The paper analyzes recombination patterns in hexaploid wheat, identifies genes controlling recombination and gene conversion, and highlights a novel RecQ helicase gene involved in gene conversion.

The paper presents and validates two capture probe sets for wheat to enable efficient and cost-effective re-sequencing of genes and promoters.

The paper identifies hidden genetic and epigenetic variation in polyploid wheat that contributes to local adaptation and suggests using methylation and transposon diversity for breeding.

The paper introduces a modified sequence capture method that enables simultaneous analysis of genotype and methylation from the same genomic DNA sample in wheat.