On Functional Annotation with Gene Co-expression Networks

Kód výsledku v IS VaVaI

RIV/68407700:21230/22:00363300 - isvavai.cz

Výsledek na webu

https://doi.org/10.1109/BIBM55620.2022.9995542

DOI - Digital Object Identifier

10.1109/BIBM55620.2022.9995542

Jazyk výsledku

angličtina

Název v původním jazyce

On Functional Annotation with Gene Co-expression Networks

Popis výsledku v původním jazyce

Gene co-expression networks have frequently been used for functional annotation. In these networks, an unknown gene is annotated with terms that have already been associated with genes whose expression profiles t end to correlate with the expression profile of the unknown gene. Despite the biological plausibility of this principle referred to as guilt-by-association, its applicability has not been thoroughly experimentally verified yet. In our paper, we formulate several statistical hypotheses concerning the principle and test them on a representative expression dataset. We demonstrate that gene annotation carried out with co-expression networks clearly outperforms random annotation and improves with increasing sample size and the knowledge of gene co-location. Eventually, we discuss the practical significance of this way of functional annotation.

Název v anglickém jazyce

On Functional Annotation with Gene Co-expression Networks

Popis výsledku anglicky

Gene co-expression networks have frequently been used for functional annotation. In these networks, an unknown gene is annotated with terms that have already been associated with genes whose expression profiles t end to correlate with the expression profile of the unknown gene. Despite the biological plausibility of this principle referred to as guilt-by-association, its applicability has not been thoroughly experimentally verified yet. In our paper, we formulate several statistical hypotheses concerning the principle and test them on a representative expression dataset. We demonstrate that gene annotation carried out with co-expression networks clearly outperforms random annotation and improves with increasing sample size and the knowledge of gene co-location. Eventually, we discuss the practical significance of this way of functional annotation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of The 2022 IEEE International Conference on Bioinformatics and Biomedicine

ISBN

978-1-6654-6819-0

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

3055-3062

Název nakladatele

IEEE Xplore

Místo vydání

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Místo konání akce

Las Vegas

Datum konání akce

6. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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