Utilizing RNA-seq Data in Monotone Iterative Generalized Linear Model to Elevate Prior Knowledge Quality of the CircRNA-miRNA-mRNA Regulatory Axis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00383693" target="_blank" >RIV/68407700:21230/25:00383693 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1186/s12859-025-06161-w" target="_blank" >https://doi.org/10.1186/s12859-025-06161-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12859-025-06161-w" target="_blank" >10.1186/s12859-025-06161-w</a>

Result language

angličtina

Original language name

Utilizing RNA-seq Data in Monotone Iterative Generalized Linear Model to Elevate Prior Knowledge Quality of the CircRNA-miRNA-mRNA Regulatory Axis

Original language description

BackgroundCurrent experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy.ResultsThis paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge.ConclusionPi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

BMC Bioinformatics

ISSN

1471-2105

e-ISSN

1471-2105

Volume of the periodical

26

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

35

Pages from-to

1-35

UT code for WoS article

001497157300001

EID of the result in the Scopus database

2-s2.0-105006726841