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Related Experiment Videos

"Super Gene Set" Causal Relationship Discovery from Functional Genomics Data.

Zongliang Yue, Michael T Neylon, Thanh Nguyen

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 25, 2018
    PubMed
    Summary
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    This study introduces a computational framework to identify causal relationships between super gene sets (pathways, annotated lists, and gene signatures). The method quantifies these regulatory links, achieving 0.81 AUC for precision and recall in identifying known relationships.

    Area of Science:

    • Computational biology
    • Systems biology
    • Genomics

    Background:

    • Identifying regulatory relationships between biological pathways is crucial for understanding complex systems.
    • Existing methods for pathway-to-pathway (PAG-to-PAG) relationships require enhancement for causal inference.

    Purpose of the Study:

    • To develop a computational framework for identifying causal relationships among pathways, annotated lists, and gene signatures (PAGs).
    • To quantify the likelihood of stimulatory or inhibitory regulatory links between PAGs.

    Main Methods:

    • Extending previous PAG-to-PAG relationship identification by incorporating gene-to-gene co-expression enrichment.
    • Developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC) to infer causal likelihood.
    • Validating the framework using a functional genomics benchmark dataset and reporting Area Under the Curve (AUC) for precision and recall.

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    Main Results:

    • The developed framework achieved an AUC of 0.81 for both precision and recall in recalling known regulatory relationships.
    • Demonstrated effectiveness in a myeloid-derived suppressor cells (MDSC) dataset.

    Conclusions:

    • The framework provides a robust method for inferring causal relationships between PAGs.
    • It aids in building multi-scale biomolecular systems models and offers new insights into regulatory networks.