Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome

See allHide authors and affiliations

Science Translational Medicine  03 Sep 2014:
Vol. 6, Issue 252, pp. 252ra123
DOI: 10.1126/scitranslmed.3009262

eLetters is an online forum for ongoing peer review. Submission of eLetters are open to all. Please read our Terms of Service before submitting your own eLetter.

Compose eLetter

Plain text

  • Plain text
    No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g.
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Statement of Competing Interests

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Vertical Tabs

  • New Approach to Effective Diagnoses

    The Zemojtel et al. paper can be extended to diagnosis of general not necessarily genetic diseases. The forerunner of the PhenIX software is the Exomiser software developed by Sanger Institute, which has been implemented by most NGS vendors. However, the output is a VCF file, which provides a list of SNPs for each individual patient with a disease to be diagnosed. Our observation is that machine learning can improve...

    Show More
    Competing Interests: None declared.

Stay Connected to Science Translational Medicine