CADA: Phenotype-driven gene prioritization based on a case-enriched knowledge graph

@inproceedings{Peng2021CADAPG,
  title={CADA: Phenotype-driven gene prioritization based on a case-enriched knowledge graph},
  author={Chengyao Peng and Simon Dieck and Alexander Schmid and Ashar Ahmad and Alexej Knaus and Maren Wenzel and Laura Mehnert and Birgit Zirn and Tobias B Haack and Stephan Ossowski and Matias Wagner and Theresa Brunet and Nadja Ehmke and Magdalena Danyel and Stanislav Rosnev and Tom Kamphans and Guy Nadav and Nicole Fleischer and Holger Fr{\"o}hlich and Peter M. Krawitz},
  booktitle={medRxiv},
  year={2021}
}
Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the human phenotype ontology (HPO), which is increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed, however, the performance of many such tools suffers from an overrepresentation of atypical cases in the medical literature… 
The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine
TLDR
The GA4GH Phenopacket schema is a freely available, community-driven standard that streamlines exchange and systematic use of phenotypic data and will facilitate sophisticated computational analysis of both clinical and genomic information to help improve the understanding of diseases and the ability to manage them.
Graph Based Link Prediction between Human Phenotypes and Genes
TLDR
This study developed a framework to predict links between human phenotype ontology (HPO) and genes using 5 different supervised machine learning algorithms and shows that the Gradient Boosting Decision Tree based model LightGBM is able to find more accurate interaction/link betweenhuman phenotype & gene pairs.

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