Explainable Deep Learning for Modeling Genomic Variation and Plant Environmental AdaptationZrimšek, Andraž (Avtor) Curk, Tomaž (Mentor) Zrimec, Jan (Komentor) large genomic modelsstate space modelsexplainable AIregulatory genomicsdeep learningenvironmental adaptationUnderstanding the link between plant environmental adaptation and genotype is a central challenge in plant biotechnology and agronomy. Here, we explore whether incorporating local DNA sequence context with genotype data improves modeling of plant environmental adaptation compared to traditional methods that rely only on single nucleotide polymorphisms (SNPs). Using Arabidopsis thaliana data, we enrich loci selected with a state-of-the-art SNP-only method, SparSNP, with SNP-centered sequence embeddings and train sparse, interpretable Elastic Net regressors. Across climate and soil variables, sequence-informed models match or surpass SNP-only baselines while using far fewer loci. Attributions, computed using the technique Integrated Gradients, link predictions to specific nucleotides, enabling the discovery of DNA motifs involved in adaptation processes, with matches to known regulators. Targeted in silico mutations at high-attribution sites drive directional shifts in predictions, yielding testable hypotheses about regulatory control. Our approach thus offers a promising avenue for genotype-phenotype prediction and, potentially, engineering stress-tolerant plants by introducing designed mutations into the DNA sequence.20252025-11-18 11:35:02Magistrsko delo/naloga176017sl