GPACDA: circRNA-disease association prediction with generating polynomials

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023736%3A_____%2F24%3A00013712" target="_blank" >RIV/00023736:_____/24:00013712 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-64629-4_3" target="_blank" >https://doi.org/10.1007/978-3-031-64629-4_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-64629-4_3" target="_blank" >10.1007/978-3-031-64629-4_3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

GPACDA: circRNA-disease association prediction with generating polynomials

Popis výsledku v původním jazyce

Circular RNA, a molecule with partially understood functions, has been implicated in various diseases. Therefore, there is a vast effort to predict associations between circular RNAs and diseases. In our recent study, we introduced circGPA, an algorithm that enables the annotation of circular RNAs with gene ontology terms through interactions with miRNAs and mRNAs. Recognizing the analytical similarity in predicting disease associations, we developed GPACDA, an extension of circGPA tailored for disease associations. The benefits of our methods include explainability, as the outputs are based on known interactions and associations, as well as the rigorous calculation of the p-value, which the circGPA algorithm can compute. We compared our method with two other tools, NCPCDA and DWNCPCDA, using a subset of the CDASOR dataset and showed that GPACDA overcomes its competitors in terms of true association ranks. Our method’s code and predictions are publicly accessible.

Název v anglickém jazyce

GPACDA: circRNA-disease association prediction with generating polynomials

Popis výsledku anglicky

Circular RNA, a molecule with partially understood functions, has been implicated in various diseases. Therefore, there is a vast effort to predict associations between circular RNAs and diseases. In our recent study, we introduced circGPA, an algorithm that enables the annotation of circular RNAs with gene ontology terms through interactions with miRNAs and mRNAs. Recognizing the analytical similarity in predicting disease associations, we developed GPACDA, an extension of circGPA tailored for disease associations. The benefits of our methods include explainability, as the outputs are based on known interactions and associations, as well as the rigorous calculation of the p-value, which the circGPA algorithm can compute. We compared our method with two other tools, NCPCDA and DWNCPCDA, using a subset of the CDASOR dataset and showed that GPACDA overcomes its competitors in terms of true association ranks. Our method’s code and predictions are publicly accessible.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

30205 - Hematology

Projekt

<a href="/cs/project/NU20-03-00412" target="_blank" >NU20-03-00412: Role transpozibilních elementů a PIWI-interagujících RNA u myelodysplastického syndromu a jejich možné klinické využití</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-303164628-7

Počet stran výsledku

16

Strana od-do

33-48

Počet stran knihy

337

Název nakladatele

Springer Verlag

Místo vydání

Berlin

Kód UT WoS kapitoly

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