David Edwards

This paper develops an unsupervised machine learning framework for variant quality recalibration in crops, enabling high-quality variant discovery without reference datasets.

This study constructs the largest wheat pangenome to identify gene variants in landraces that are absent in modern cultivars, highlighting their potential for improving wheat resilience.

This study sequenced two wild lupin species to explore their genomic diversity and evolutionary insights, revealing gene family expansions linked to seed size and adaptation.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

crosshap visualizes local haplotype structures and associations with traits using density-based clustering and visualization tools.

The paper introduces a pangenome analysis pipeline (PSVCP) for rice that improves functional gene identification by capturing structural variations and provides insights into genomic diversity and trait associations.

This paper introduces a DNABERT-based NLP model to identify lncRNAs from genomic sequences across plant species, achieving accuracies between 57.9% and 83.4%.

This study develops a sorghum pan-genome to explore its functional utility in accelerating genetic gain by identifying genes and SNPs associated with agronomic traits and drought tolerance.

RefKA assembles large complex genomes using long-read data with reduced computing requirements by binning a reference genome and parallelizing assembly steps.

This study validates large-scale structural variations detected by optical mapping in subterranean clover using Illumina short reads, showing BOM detects total SV sizes but lacks precision in location and quantity.