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

Journal: Genetics in medicine : official journal of the American College of Medical Genetics
Published Date:

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.

Authors

  • Qigang Li
    GenomCan Inc., Chengdu, Sichuan, China.
  • Keyan Zhao
    GenomCan Inc., Chengdu, Sichuan, China.
  • Carlos D Bustamante
    Department of Genetics, Stanford University, Stanford, CA, USA.
  • Xin Ma
    Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
  • Wing H Wong
    Department of Statistics, Stanford University, Stanford, 94305, CA, USA. whwong@stanford.edu.