Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.

ABSTRACT:

Purpose

Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision, and noise in disease phenotype descriptions and insufficient utilization of expert knowledge in clinical genetics. To overcome these difficulties, we present a novel method called Xrare for the prioritization of causative gene variants in rare disease diagnosis.

Methods

We propose a new phenotype similarity scoring method called Emission-Reception Information Content (ERIC), which is highly tolerant of noise and imprecision in clinical phenotypes. We utilize medical genetic domain knowledge by designing genetic features implemen

SUBMITTER: Li Q 

PROVIDER: S-EPMC6752318 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature