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We developed network embedding algorithms to analyze complex omics data. These methods identify new cancer-related genes and protein complexes by analyzing molecular network structures, aiding biomedical discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Accumulating complex omics data presents challenges in extracting biomedical information.
  • Network embedding algorithms represent biological macromolecules as vectors for knowledge extraction.
  • Positive Pointwise Mutual Information (PPMI) matrices are implicitly factorized by neural networks for embeddings.

Purpose of the Study:

  • To introduce network embedding algorithms for analyzing complex omics data.
  • To represent the human protein-protein interaction (PPI) network using PPMI and graphlet degree vector PPMI matrices.
  • To extract new biomedical knowledge, predict genes in protein complexes, and identify cancer-related genes.

Main Methods:

  • Generating embeddings by decomposing PPMI matrices with Nonnegative Matrix Tri-Factorization.
  • Representing the human protein-protein interaction (PPI) network using PPMI and graphlet degree vector PPMI matrices.
  • Utilizing cosine similarities between gene embedding vectors to predict novel gene functions and associations.

Main Results:

  • Genes embedded closely in the generated spaces exhibit similar biological functions.
  • Successfully predicted new genes participating in protein complexes.
  • Identified novel cancer-related genes, with 80% validated in literature and 93.3% showing potential clinical relevance as biomarkers.

Conclusions:

  • Network embedding algorithms effectively mine complex omics data for biomedical insights.
  • The proposed PPMI-based approach captures topological similarities in molecular networks.
  • This method facilitates the discovery of novel gene functions and cancer biomarkers.