Using empirical biological knowledge to infer regulatory networks from multi-omics data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F22%3A00126695" target="_blank" >RIV/00216224:14310/22:00126695 - isvavai.cz</a>

Result on the web

<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04891-9" target="_blank" >https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04891-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12859-022-04891-9" target="_blank" >10.1186/s12859-022-04891-9</a>

Result language

angličtina

Original language name

Using empirical biological knowledge to infer regulatory networks from multi-omics data

Original language description

Background Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. Results We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. Conclusions We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.

Czech name

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Czech description

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Type

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

CEP classification

—

OECD FORD branch

10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2022

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

23

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

1-23

UT code for WoS article

000843131200002

EID of the result in the Scopus database

2-s2.0-85136182199