IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00131143" target="_blank" >RIV/00216224:14310/23:00131143 - isvavai.cz</a>
Result on the web
<a href="https://www.liebertpub.com/doi/10.1089/cmb.2022.0149" target="_blank" >https://www.liebertpub.com/doi/10.1089/cmb.2022.0149</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1089/cmb.2022.0149" target="_blank" >10.1089/cmb.2022.0149</a>
Alternative languages
Result language
angličtina
Original language name
IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data
Original language description
Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is an empirical biological knowledge estimation from the available experimental data, which complements the missing biological prior knowledge. IntOMICS has the potential to be a powerful resource for exploratory systems biology.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
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
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Computational Biology
ISSN
1066-5277
e-ISSN
1557-8666
Volume of the periodical
30
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
569-574
UT code for WoS article
000955812400001
EID of the result in the Scopus database
2-s2.0-85159543683