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@article{Osborne:2009p4571,
author = {John D Osborne and Jared Flatow and Michelle Holko and Simon M Lin and Warren A Kibbe and Lihua Julie Zhu and Maria I Danila and Gang Feng and Rex L Chisholm}, 
journal = {BMC Genomics},
title = {Annotating the human genome with Disease Ontology},
abstract = {The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.},
affiliation = {Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA. ozborn@uab.edu},
pages = {S6},
volume = {10 Suppl 1},
year = {2009},
month = {Jan},
language = {eng},
keywords = {Software, Databases: Genetic, Unified Medical Language System, Humans, Genome: Human, Computational Biology}, 
date-added = {2012-05-30 12:38:02 -0400},
date-modified = {2012-05-30 12:38:25 -0400},
doi = {10.1186/1471-2164-10-S1-S6},
pii = {1471-2164-10-S1-S6},
pmid = {19594883},
URL = {http://www.biomedcentral.com/1471-2164/10/S1/S6},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2009/Osborne/Bmc%20Genomics%202009%20Osborne.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p4571},
rating = {0}
}


@article{Kircher:2014p5681,
author = {Martin Kircher and Daniela M Witten and Preti Jain and Brian J O'Roak and Gregory M Cooper and Jay Shendure},
journal = {Nat Genet},
title = {A general framework for estimating the relative pathogenicity of human genetic variants},
abstract = {Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.},
affiliation = {Department of Genome Sciences, University of Washington, Seattle, Washington, USA.},
pages = {},
year = {2014},
month = {Feb},
language = {ENG},
date-added = {2014-02-04 12:09:10 -0500},
date-modified = {2014-02-04 12:09:10 -0500},
doi = {10.1038/ng.2892},
pii = {ng.2892},
pmid = {24487276},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2014/Kircher/Nat%20Genet%202014%20Kircher.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p5681},
read = {Yes},
rating = {0}
}

