MORE interpretable multi-omic regulatory networks to characterize phenotypes

The identification of phenotype-specific regulatory mechanisms is crucial for understanding the molecular basis of diseases and other complex traits. However, the lack of tools capable of constructing multi-omic, condition-specific regulatory networks remains a significant limitation. He re, we introduce MO RE (Multi-Omics Regulation), a novel R package for the inference and comparison of multi-modal regulatory networks publicly available at https://github.com/BiostatOmics/MORE. MORE supports any number and type of omics layers, integrates prior regulatory knowledge, and employs advanced regression-based modelling and variable selection techniques to identify significant regulators of target features. We evaluated MORE on simulated datasets and benchmarked it against state-of-the-art tools. Our tool exhibited superior accuracy in identifying key regulators, model goodness-of-fit, and computational efficiency. Additionally, we applied MORE to an ovarian cancer dataset to uncover tumour subtype-specific regulatory mechanisms associated with distinct survival outcomes. By providing a comprehensive and user-friendly framework for constructing phenotype-specific regulatory networks, MORE addresses a critical gap in the field of multi-omics data integration. Its versatility and effectiveness make it a valuable resource for advancing our understanding of complex molecular interactions and regulatory systems.

• Publication year

  2024

• Venue

  bioRxiv

• Publication date

  2024-12-21

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1101/2024.01.25.577162
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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