| Literature DB >> 32939051 |
Edison Ong1, Lucy L Wang2, Jennifer Schaub3, John F O'Toole4,5, Becky Steck3, Avi Z Rosenberg6, Frederick Dowd7, Jens Hansen8,9, Laura Barisoni10, Sanjay Jain11, Ian H de Boer12, M Todd Valerius13, Sushrut S Waikar14, Christopher Park15, Dana C Crawford16,17,18, Theodore Alexandrov19,20, Christopher R Anderton21, Christian Stoeckert22, Chunhua Weng23, Alexander D Diehl24, Christopher J Mungall25, Melissa Haendel26, Peter N Robinson27, Jonathan Himmelfarb12,15, Ravi Iyengar8,9, Matthias Kretzler1,3, Sean Mooney28, Yongqun He29,30,31.
Abstract
An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.Entities:
Mesh：
Year: 2020 PMID： 32939051 PMCID： PMC8012202 DOI： 10.1038/s41581-020-00335-w
Source DB: PubMed Journal: Nat Rev Nephrol ISSN： 1759-5061 Impact factor: 28.314