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Disease classification via gene network integrating modules and pathways

  • Peng Cheng Laboratory
  • School of Computer Science and Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing
  • Beihang University
  • LMIB & School of Mathematics and System Science

Research output: Contribution to journal › Article › peer-review

Abstract

Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.

Original languageEnglish
Article number190214
JournalRoyal Society Open Science
Volume6
Issue number7
DOIs
StatePublished - 1 Jul 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Disease classification
  • Gene network
  • Modules
  • Pathways

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