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Related Concept Videos

Protein Networks02:26

Protein Networks

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Related Experiment Video

Updated: Sep 16, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Generalized information criteria for personalized gene network inference.

Heewon Park1,2,3,4, Seiya Imoto3, Sadanori Konishi5

  • 1School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Republic of Korea.

Frontiers in Genetics
|July 7, 2025
PubMed
Summary

We developed a new evaluation criterion for personalized gene network analysis, improving target identification for therapies. This method aids in understanding drug resistance mechanisms in cancers like acute myeloid leukemia (AML).

Keywords:
acute myeloid leukemiagastri cancergeneralized information criteriamodel evaluationpersonalized gene network

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Personalized therapies require identifying individual genomic characteristics.
  • Gene network analysis is crucial for understanding complex biological systems and developing targeted treatments.
  • Existing methods for parameter selection in regularized modeling are computationally expensive or unsuitable for certain estimation techniques.

Purpose of the Study:

  • To develop a novel evaluation criterion for personalized gene network analysis using kernel-based L1-type regularization.
  • To overcome the limitations of traditional parameter selection methods like cross-validation, AIC, and BIC.
  • To identify personalized therapeutic targets and understand drug resistance mechanisms in cancer.

Main Methods:

  • Kernel-based L1-type regularization for personalized gene network analysis.
  • Introduction of a novel generalized information criterion (GIC) suitable for various estimation techniques.
  • Monte Carlo simulations to evaluate the performance of the proposed GIC.

Main Results:

  • The proposed GIC outperforms existing criteria in edge selection and weight estimation for personalized gene networks.
  • Analysis of acute myeloid leukemia (AML) drug sensitivity revealed PIK3CD activation and RARA/RELA suppression as key markers for chemotherapy efficacy.
  • Personalized therapeutic targets were uncovered for gastric cancer drug sensitivity analysis.

Conclusions:

  • The proposed sample-specific GIC is a valuable tool for evaluating personalized modeling, particularly in sample characteristic-specific gene network analysis.
  • This approach facilitates the discovery of critical molecular interactions for personalized cancer therapies.
  • The findings provide insights into AML drug resistance mechanisms and potential therapeutic strategies.