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November 11, 2025Frontiers in Molecular BiosciencesOpen Access

Identification and validation of potential shared diagnostic markers for sepsis-induced ARDS and cardiomyopathy via WGCNA and machine learning

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Authors

JWJing WeiBHBaoyue HuangKHKunlin Hu

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Overview

Machine learning reveals diagnostic biomarkers for sepsis-induced cardiomyopathy and ARDS, suggesting treatment avenues.

Key Points

  • This research identifies biomarkers associated with sepsis-induced acute respiratory distress syndrome and cardiomyopathy.
  • Conducted weighted gene co-expression network analysis
  • Utilized machine learning techniques including support vector machine and random forest
  • Employed artificial neural network for diagnostic performance
  • Assessed immune responses using CIBERSORT
  • Identified potential therapeutic compounds from PubChem.
  • Five key biomarkers were identified: LCN2, AIF1L, STAT3, SOCS3, and SDHD.
  • SOCS3 demonstrated significant diagnostic value and correlation with immune cells.
  • Machine learning effectively selected hub genes associated with sepsis complications.

Cite This Study

Wei et al. (2025) studied this question.

synapsesocial.com/papers/69252e9ec0ce034ddc35644bhttps://doi.org/10.3389/fmolb.2025.1665387
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