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Biomedical data, computational methods and tools for evaluating disease-disease associations.

Ju Xiang1, Jiashuai Zhang2, Yichao Zhao1

  • 1School of Computer Science and Engineering, Central South University, China.

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|February 9, 2022
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Summary

This review summarizes computational methods and tools for analyzing disease-disease associations using diverse biomedical data. It highlights how these approaches advance understanding of complex diseases and aid in discovering new treatments.

Keywords:
biomedical datacomputational methodsdisease–disease associationssoftware tools

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genomics

Background:

  • Disease-relationship exploration is a key research area, driven by increasing biomedical data.
  • Understanding complex human diseases requires integrating phenotypic and molecular data.
  • Computational methods offer valuable insights into disease mechanisms and treatments.

Purpose of the Study:

  • To systematically review biomedical data, databases, and computational methods for evaluating disease-disease associations.
  • To classify existing computational methods based on data types used.
  • To summarize available software tools and platforms for disease association analysis.

Main Methods:

  • Literature review and synthesis of existing research on disease-disease associations.
  • Classification of computational methods into five categories: semantic-based, phenotype-based, function-based, representation learning-based, and text mining-based.
  • Summary of relevant biomedical data sources and analytical tools.

Main Results:

  • A comprehensive overview of data types and databases for disease association studies.
  • Categorization of computational methods, detailing their data utilization.
  • Identification of key software and platforms for disease-disease association computation and analysis.

Conclusions:

  • This review provides a systematic overview of the current landscape of disease-disease association research.
  • It emphasizes the importance of computational methods and tools in advancing the study of complex diseases.
  • The findings can guide future development and application of disease association analysis tools.