Similarity Network Fusion: A Novel Application to Making Clinical Diagnoses

Andréanne N Zizzo; Lauren Erdman; Brian M Feldman; Anna Goldenberg

doi:10.1016/j.rdc.2018.01.005

Similarity Network Fusion: A Novel Application to Making Clinical Diagnoses

Rheum Dis Clin North Am. 2018 May;44(2):285-293. doi: 10.1016/j.rdc.2018.01.005. Epub 2018 Feb 21.

Authors

Andréanne N Zizzo¹, Lauren Erdman², Brian M Feldman³, Anna Goldenberg²

Affiliations

¹ Department of Pediatrics, Division of Gastroenterology and Hepatology, Western University, Children's Hospital, London Health Sciences Centre, 800 Commissioners Road East, B1-162, London, Ontario N6A 5W9, Canada. Electronic address: andreanne.zizzo@lhsc.on.ca.
² Genetics and Genome Biology, Department of Computer Science, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning (PGCRL), University of Toronto, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada.
³ Department of Pediatrics, Division of Rheumatology, University of Toronto, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.

PMID: 29622295
DOI: 10.1016/j.rdc.2018.01.005

Abstract

Similarity Network Fusion (SNF) is a novel methodological tool that integrates multiple different types of data to identify homogeneous subsets of patients in whom disease classification may be otherwise unclear or challenging. In this review article, the authors hope to provide insight into how SNF can be used in clinical decision making where the aim is to have little influence on the data prior to obtaining the results of the analysis.

Keywords: Analysis; Classification; Immune-mediated disease; Similarity network fusion.

Publication types

Review

MeSH terms

Autoimmune Diseases / diagnosis*
Cluster Analysis
Computational Biology*
Diagnosis, Computer-Assisted / methods*
Humans