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Heterogeneous network embedding for identifying symptom candidate genes.

Kuo Yang1, Ning Wang1, Guangming Liu1

  • 1School of Computer and Information Technology and Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for identifying symptom genes by analyzing symptom-gene associations. The approach significantly improves the precision and recall of candidate gene identification, aiding precision medicine.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Precision Medicine

Background:

  • Understanding the molecular basis of symptoms is crucial for personalized disease management.
  • Identifying symptom-associated genes is less explored compared to disease-gene associations.
  • Existing methods for gene identification lack efficiency in pinpointing symptom-specific genes.

Purpose of the Study:

  • To develop a computational method for identifying genes associated with specific symptoms.
  • To create a high-quality benchmark dataset of symptom-gene associations.
  • To advance precision medicine by refining disease understanding and management.

Main Methods:

  • Constructed a heterogeneous symptom-related network integrating various associations.
  • Applied a network embedding algorithm to generate low-dimensional vector representations of symptoms and genes.
  • Calculated vector similarities to measure symptom-gene relevance and identify candidate genes.

Main Results:

  • Curated a benchmark dataset with 18,270 symptom-gene associations.
  • The proposed heterogeneous network embedding method significantly outperformed baseline algorithms (FSGER, PRINCE).
  • Achieved substantial improvements in precision (66.80%) and recall (53.96%) for candidate gene identification.

Conclusions:

  • The developed method demonstrates excellent performance in identifying symptom-associated genes.
  • Created a prediction dataset of 17,479 symptom-candidate genes for further research.
  • The curated datasets and method have the potential to accelerate the investigation of molecular symptom mechanisms.