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Biological organization is the classification of biological structures, ranging from atoms at the bottom of the hierarchy to the Earth's biosphere. Each level of the hierarchy represents an increase in complexity that builds upon the previous level.
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Decoding multilevel relationships with the human tissue-cell-molecule network.

Siyu Hou1, Peng Zhang1, Kuo Yang1,2

  • 1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China.

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|May 13, 2022
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Summary
This summary is machine-generated.

This study introduces Graph Local InfoMax (GLIM), a novel framework for uncovering complex relationships between diseases, tissues, cell types, and molecules. GLIM enhances disease gene prediction and reveals biological mechanisms, such as those in gastric cancer.

Keywords:
cell interactionsdisease gene predictionmultilevel networknetwork embeddingtissue-cell-molecule

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Area of Science:

  • Systems biology
  • Computational biology
  • Genomics

Background:

  • Understanding molecular functions in specific human tissues/cells is vital for physiology and disease insights.
  • Challenges exist in systematically linking heterogeneous and incomplete data across disease phenotypes, tissues, cell types, and molecules.

Purpose of the Study:

  • To develop a methodological framework for systematically uncovering associations among multilevel biological elements.
  • To address the heterogeneity and incompleteness of data in biological networks.

Main Methods:

  • Introduction of a human multilevel network (HMLN) incorporating multiple tissues and cell types.
  • Application of Graph Local InfoMax (GLIM), a contrastive learning-based framework, to embed HMLN features.
  • Systematic mining of potential relationships between multilevel elements.

Main Results:

  • GLIM demonstrated superior performance in disease gene prediction compared to state-of-the-art algorithms.
  • GLIM successfully inferred cell markers and rewired intercellular/molecular interactions.
  • The framework uncovered the tissue-cell-molecule network underlying gastritis and gastric cancer.

Conclusions:

  • The developed methodological framework has the potential to systematically uncover complex disease mechanisms.
  • GLIM facilitates mining high-quality relationships among phenotypical, tissue, cellular, and molecular elements.
  • This approach provides systematic insights into disease occurrence and development, exemplified by gastric cancer.