AI Article Synopsis
- Integration of multi-omics data allows for a deeper understanding of complex biological systems by analyzing various molecular components together.
- The IntOMICS R/Bioconductor package uses a Bayesian framework and Markov Chain Monte Carlo sampling to combine gene expression, DNA methylation, and other data types while also incorporating biological knowledge.
- A key advantage of IntOMICS is its ability to estimate biological knowledge from experimental data, filling gaps in prior understanding and enhancing systems biology research.
Article Abstract
Integration of multi- 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- 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 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178929 | PMC |
| http://dx.doi.org/10.1089/cmb.2022.0149 | DOI Listing |
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