scholarly journals Peer Review #1 of "HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences (v0.1)"

2018 ◽  
Author(s):  
I Kahanda
Keyword(s):  
Peer Review ◽  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Human Phenotype

scholarly journals PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 259 ◽  
Author(s):  
Indika Kahanda ◽  
Christopher Funk ◽  
Karin Verspoor ◽  
Asa Ben-Hur
Keyword(s):  
Large Scale ◽  
Heterogeneous Data ◽  
Human Phenotype Ontology ◽  
Data Sources ◽  
Literature Mining ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Protein Coding ◽  
Human Phenotype ◽  
Heterogeneous Data Sources

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis

2019 ◽  
Author(s):  
Radoslav Davidović ◽  
Vladimir Perovic ◽  
Branislava Gemovic ◽  
Nevena Veljkovic
Keyword(s):  
Gene Ontology ◽  
Source Code ◽  
Enrichment Analysis ◽  
Human Phenotype Ontology ◽  
Supplementary Information ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Human Phenotype ◽  
Term Enrichment Analysis ◽  
Term Enrichment

Abstract Summary Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. Availability and Implementation DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php Supplementary information Supplementary data are available at Bioinformatics online.

Predicting genes from phenotypes using Human Phenotype Ontology (HPO) terms

2021 ◽  
Vol 132 ◽  
pp. S149
Author(s):  
Anne Slavotinek ◽  
Hannah Prasad ◽  
Hannah Hoban ◽  
Tiffany Yip ◽  
Shannon Rego ◽  
...  
Keyword(s):  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype

scholarly journals Decomposing Phenotype Descriptions for the Human Skeletal Phenome

2013 ◽  
Vol 6 ◽  
pp. BII.S10729
Author(s):  
Tudor Groza ◽  
Jane Hunter ◽  
Andreas Zankl
Keyword(s):  
Experimental Study ◽  
Intrinsic Value ◽  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Generic Concept ◽  
Meta Model ◽  
Processing Pipeline ◽  
Skeletal Phenotype ◽  
Automatic Decomposition

Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. We use this approach to showcase the usefulness of the generic concept of phenotype decomposition by performing an experimental study on all skeletal phenotype concepts defined in the Human Phenotype Ontology.

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