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Updated: May 30, 2026
03:37
Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
Published on: March 1, 2024
Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function
Vincenzo Belcastro1, Velia Siciliano, Francesco Gregoretti
1Telethon Institute of Genetics and Medicine, Via P. Castellino, Naples, Italy. dibernardo@tigem.it
Nucleic Acids Research
|July 26, 2011
Summary
Researchers mapped gene expression across human and mouse samples, revealing gene networks and functions. This analysis identified gene relationships, predicted protein functions, and uncovered the role of the GRN gene in lysosome function.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene expression profiles (GEPs) provide insights into cellular function.
- Understanding gene relationships is crucial for deciphering complex biological processes.
- Large-scale datasets are essential for building comprehensive gene networks.
Purpose of the Study:
- To construct extensive gene regulatory networks for both human and mouse.
- To identify conserved gene interactions between species.
- To develop a tool for exploring these networks and predicting gene function.
Main Methods:
- Collected and analyzed a large dataset of human (20,255) and mouse (8,895) GEPs.
- Employed a mutual information (MI) reverse-engineering approach to infer gene-gene relationships.
- Validated inferred interactions and identified co-expressed gene modules.
Main Results:
- Generated human gene networks with over 4.8 million connections and mouse networks with over 14.4 million connections.
- Identified 12% inter-species conservation in gene networks.
- Discovered functional gene modules and experimentally validated protein-protein interactions.
- Confirmed the role of the granulin precursor (GRN) gene in lysosome function.
Conclusions:
- The developed gene networks offer a valuable resource for biological research.
- The approach can predict gene function, subcellular localization, and disease gene roles.
- The study provides a platform for exploring gene interactions and functions through an online tool.
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