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Identifying diseases-related metabolites using random walk.

Yang Hu1, Tianyi Zhao1, Ningyi Zhang1

  • 1School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.

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This study introduces a novel network-based method to identify new metabolic markers for diseases. The approach effectively prioritizes metabolite-disease pairs, aiding in metabolic disease diagnosis and prevention.

Keywords:
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Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Metabolic diseases are linked to disruptions in the human body's metabolites.
  • Metabolites offer a more accessible target for disease diagnosis and prevention than genetic factors.
  • Numerous disease-associated metabolites remain undiscovered, necessitating advanced exploration methods.

Purpose of the Study:

  • To develop and validate a novel computational method for prioritizing metabolite-disease associations.
  • To identify novel metabolic markers for improved disease diagnosis and prevention strategies.
  • To construct a weighted metabolite association network (WMAN) for predictive analysis.

Main Methods:

  • Extracted metabolite-disease associations from the Human Metabolome Database (HMDB) using text mining.
  • Calculated metabolite similarity based on shared associated diseases.
  • Constructed a weighted metabolite association network (WMAN) and employed random walk for novel marker prediction.

Main Results:

  • A WMAN was built using 453 metabolites and their pairwise similarities.
  • The network's predictive performance was validated, achieving a high area under the receiver operating characteristic curve (AUC) of 0.7048.
  • Case studies successfully identified novel metabolites associated with diabetes mellitus, validated by recent research.

Conclusions:

  • A reliable method for prioritizing metabolite-disease pairs has been presented.
  • The validated network demonstrates effectiveness in discovering novel metabolic disease markers.
  • This approach supports advancements in metabolic disease research and clinical applications.