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LAMP: disease classification derived from layered assessment on modules and pathways in the human gene network.

Zhilong MiBing-Hui GuoXiaobo YangZiqiao YinZhiming Zheng
Published in: BMC bioinformatics (2020)
In this work, by collecting data from BioGRID and KEGG, we develop a disease classification model LAMP, to support people to view diseases from the perspective of commonalities in etiology and pathology. Comprehensive research on existing diseases can help meet the challenges of unknown diseases. The results provide suggestions for combination diagnosis and gene-targeted therapy, which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies.
Keyphrases
  • machine learning
  • deep learning
  • endothelial cells
  • copy number
  • genome wide
  • palliative care
  • gene expression
  • stem cells
  • big data
  • electronic health record
  • gold nanoparticles
  • mesenchymal stem cells