Show document

A+ | A- | | SLO | ENG

Title: Arabidopsis tissue- and perturbation-specific gene expression resource : version v2 Authors: ID Modic, Vid (Author) ID Zrimec, Jan (Author) Files: URL - Source URL, visit https://zenodo.org/records/18714194   ZIP - Research data, download (1,24 GB) MD5: C359D7DDB6F5E74C4E022CF28AF05FD3   JSON - Metadata, download (3,81 KB) MD5: A3170C0723FE2E3EE6E9ED0214A3863A   This document has even more files. Complete list of files is available below. Language: English Typology: 2.20 - Complete scientific database of research data Organization: NIB - National Institute of Biology Abstract: 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. Keywords: transcriptomic data, bioinformatics, Arabidopsis Publication status: Published Publication version: Version of Record Publication date: 20.02.2026 Place of publishing: Genève Publisher: Zenodo Year of publishing: 2026 Number of pages: 1 spletni vir PID: 20.500.12556/DiRROS-29522  UDC: 577.2 DOI: 10.5281/zenodo.18714194  COBISS.SI-ID: 272904451  Note: Nasl. z. nasl. zaslona; Opis vira z dne 23. 3. 2026; Publication date in DiRROS: 22.05.2026 Views: 85 Downloads: 104 Metadata:    Cite this work Plain text BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Copy citation

Share: Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Document is financed by a project

Funder:	ARIS - Slovenian Research and Innovation Agency
Project number:	J2-3060
Name:	Sistemsko biološko podprto globoko učenje za interpretacijo načel regulacije rasti in obrambe rastlin

Licences

License:	GPL 3.0, GNU General Public License, version 3
Link:	https://www.gnu.org/licenses/gpl-3.0.html
Description:	You may copy, distribute and modify the software as long as you track changes/dates of in source files and keep modifications under GPL. You can distribute your application using a GPL library commercially, but you must also provide the source code. GPL 3 tries to close some loopholes in GPL 2.

Secondary language

Language:	Slovenian
Keywords:	transkriptomski podatki, bioinformatika, arabidopsis

Files

Loading...

Back