Izpis gradiva

A+ | A- | | SLO | ENG

Naslov: Arabidopsis tissue- and perturbation-specific gene expression resource : version v2 Avtorji: ID Modic, Vid (Avtor) ID Zrimec, Jan (Avtor) Datoteke: URL - Izvorni URL, za dostop obiščite https://zenodo.org/records/18714194   ZIP - Raziskovalni podatki, prenos (1,24 GB) MD5: C359D7DDB6F5E74C4E022CF28AF05FD3   JSON - Metapodatki, prenos (3,81 KB) MD5: A3170C0723FE2E3EE6E9ED0214A3863A   To gradivo ima še več datotek. Celoten seznam je na voljo spodaj. Jezik: Angleški jezik Tipologija: 2.20 - Zaključena znanstvena zbirka raziskovalnih podatkov Organizacija: NIB - Nacionalni inštitut za biologijo Povzetek: 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. Ključne besede: transcriptomic data, bioinformatics, Arabidopsis Status publikacije: Objavljeno Verzija publikacije: Objavljena publikacija Datum objave: 20.02.2026 Kraj izida: Genève Založnik: Zenodo Leto izida: 2026 Št. strani: 1 spletni vir PID: 20.500.12556/DiRROS-29522  UDK: 577.2 DOI: 10.5281/zenodo.18714194  COBISS.SI-ID: 272904451  Opomba: Nasl. z. nasl. zaslona; Opis vira z dne 23. 3. 2026; Datum objave v DiRROS: 22.05.2026 Število ogledov: 83 Število prenosov: 101 Metapodatki:    Citiraj gradivo Navadno besedilo BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Kopiraj citat

Objavi na: Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše podrobnosti ali sproži prenos.

Gradivo je financirano iz projekta

Financer:	ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:	J2-3060
Naslov:	Sistemsko biološko podprto globoko učenje za interpretacijo načel regulacije rasti in obrambe rastlin

Licence

Licenca:	GPL 3.0, GNU General Public License, version 3
Povezava:	https://www.gnu.org/licenses/gpl-3.0.html
Opis:	Ta licenca dovoljuje kopiranje, razširjanje in spreminjanje, dokler se sledi spremembam in datumom v izvornih datotekah in obdrži vse spremembe pod GPL. Aplikacije s to licenco lahko komercialno distribuirate, vendar morate razkriti tudi izvorno kodo. GPL 3 poskuša zapreti nekaj vrzeli v GPL 2.

Sekundarni jezik

Jezik:	Slovenski jezik
Ključne besede:	transkriptomski podatki, bioinformatika, arabidopsis

Datoteke

Podatki se nalagajo...

Nazaj