RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis.

ABSTRACT: DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outco

SUBMITTER: Jia J 

PROVIDER: S-EPMC6288202 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature