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Network‑based gene function inference method to predict optimal gene functions associated with fetal growth

Ke-Jun Ye1, Jie Dai1, Ling-Yun Liu2

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This study used a network-based guilt by association method to identify key gene functions involved in fetal growth restriction (FGR). Optimal gene functions identified may aid in early detection and targeted treatment of FGR.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Fetal growth restriction (FGR) is a leading cause of perinatal mortality.
  • Network-based approaches enhance gene function prediction accuracy.
  • Guilt by association (GBA) is a principle for inferring gene function.

Purpose of the Study:

  • To predict optimal gene functions associated with FGR using a network-based GBA method.
  • To identify potential biomarkers for FGR diagnosis and treatment.

Main Methods:

  • Identification of differentially-expressed genes (DEGs) in FGR patients.
  • Construction of a co-expression network (CEN) using DEGs and Spearman correlation.
  • Application of GBA algorithm with Gene Ontology (GO) terms and AUC calculation.

Main Results:

  • 115 DEGs and 109 GO terms were analyzed.
  • 78 GO terms showed good classification performance (AUC >0.5).
  • Five optimal GO terms (defense response, immune system process, response to stress, cellular response to chemical stimulus, positive regulation of biological process) had AUC >0.7.

Conclusions:

  • The study provides insights into FGR's pathological mechanisms.
  • Identified optimal gene functions may serve as potential biomarkers for FGR.
  • Further research is needed to clarify interactions between the identified GO terms.