TY - JOUR
T1 - GeneYenta: A Phenotype-Based Rare Disease Case Matching Tool Based on Online Dating Algorithms for the Acceleration of Exome Interpretation
AU - Gottlieb, Michael M.
AU - Arenillas, David J.
AU - Maithripala, Savanie
AU - Maurer, Zachary D.
AU - Tarailo Graovac, Maja
AU - Armstrong, Linlea
AU - Patel, Millan
AU - van Karnebeek, Clara
AU - Wasserman, Wyeth W.
PY - 2015
Y1 - 2015
N2 - Advances in next-generation sequencing (NGS) technologies have helped reveal causal variants for genetic diseases. In order to establish causality, it is often necessary to compare genomes of unrelated individuals with similar disease phenotypes to identify common disrupted genes. When working with cases of rare genetic disorders, finding similar individuals can be extremely difficult. We introduce a web tool, GeneYenta, which facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. Importantly, the system is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes, inspired by online dating services, uses an ontologybased semantic case matching algorithm with attribute weighting. We evaluate the capacity of the system using a curated reference data set and 19 clinician entered cases comparing four matching algorithms. We find that the inclusion of clinician weights can augment phenotype matching
AB - Advances in next-generation sequencing (NGS) technologies have helped reveal causal variants for genetic diseases. In order to establish causality, it is often necessary to compare genomes of unrelated individuals with similar disease phenotypes to identify common disrupted genes. When working with cases of rare genetic disorders, finding similar individuals can be extremely difficult. We introduce a web tool, GeneYenta, which facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. Importantly, the system is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes, inspired by online dating services, uses an ontologybased semantic case matching algorithm with attribute weighting. We evaluate the capacity of the system using a curated reference data set and 19 clinician entered cases comparing four matching algorithms. We find that the inclusion of clinician weights can augment phenotype matching
U2 - https://doi.org/10.1002/humu.22772
DO - https://doi.org/10.1002/humu.22772
M3 - Article
C2 - 25703386
SN - 1059-7794
VL - 36
SP - 432
EP - 438
JO - Human mutation
JF - Human mutation
IS - 4
ER -