HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal: Journal of biomedical informatics
Published Date:

Abstract

BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph.

Authors

  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • Suyuan Peng
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; The Second Clinical College Guangzhou University of Chinese Medicine, China.
  • Yadan Fan
    Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
  • Andrew Wen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
  • Sijia Liu
    These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Liwei Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.