20.500.12556/DiRROS-29522 Arabidopsis tissue- and perturbation-specific gene expression resource version v2 A prerequisite to understanding how an organism functions and responds to its environment is to determine which gene expression patterns are associated with a specific tissue type or perturbation response. Neural network-based methods can provide subsets of highly informative genes for such associations. Here, we propose that integrating prior molecular knowledge related to gene expression within deep neural networks can lead to improved identification of tissue and perturbation-related gene sets. We first construct an Arabidopsis tissue- and perturbation-specific gene expression resource from published data, and address batch effects by implementing and evaluating several approaches, including Conditional Variational Autoencoders. We then incorporate prior published molecular knowledge on protein-DNA and protein-protein interactions as additional model layers, training the models to classify tissue types and perturbation groups according to input gene expression patterns. Although knowledge graph-based models achieve similar classification performance as baseline models, the analysis of model explainability demonstrates that they outperform the baseline models by prioritising biologically relevant genes. The identified genes are shown to be related to the specific tissue types and molecular processes following the particular perturbations. Our results demonstrate the applicability and reliability improvements of knowledge graph-primed deep learning for identifying condition-specific genes and gene sets. transcriptomic data bioinformatics Arabidopsis transkriptomski podatki bioinformatika arabidopsis true false false Zenodo Angleški jezik Slovenski jezik Neznano 2026-05-22 12:19:10 2026-05-22 12:32:17 2026-05-23 04:01:47 0000-00-00 00:00:00 2026 0 Genève 0 Nasl. z. nasl. zaslona; Opis vira z dne 23. 3. 2026; 1 spletni vir 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2026-02-20 577.2 10.5281/zenodo.18714194 272904451 https://zenodo.org/records/18714194 1 ad3bb5f4-55c7-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44300 RAZ_Modic_Vid_2026.zip RAZ_Modic_Vid_2026.zip 1 C359D7DDB6F5E74C4E022CF28AF05FD3 b54020ebb51057c2d9c9f6d4aa392ac0e0544cbc45cd8c55ed643057e2c8bce8 f8d6efc8-55c8-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44301 RAZ_Modic_Vid_2026.json RAZ_Modic_Vid_2026.json 1 A3170C0723FE2E3EE6E9ED0214A3863A eff75db24dc45eaac04cf7cc8bb4edb125193114ab4580244877dbac03af7268 4cd09459-55c9-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44303 https://github.com/KertAles/GeneIdentifier 0 3c1e4429-55c9-11f1-8a5a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44302 Nacionalni inštitut za biologijo 0 0 0