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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Published on: February 23, 2019

Integrating phenotype ontologies with PhenomeNET.

Miguel Ángel Rodríguez-García^1,2, Georgios V Gkoutos^3,4,5, Paul N Schofield⁶

¹Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia.

Journal of Biomedical Semantics

|December 21, 2017

Summary

This summary is machine-generated.

Integrating phenotype data across species is challenging. PhenomeNET uses automated reasoning to align phenotype ontologies, improving disease gene discovery by linking model organism findings to human conditions.

Keywords:

Automated reasoning Disease gene prioritization OWL PhenomeNET Phenotype

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Area of Science:

Comparative genomics
Bioinformatics
Systems biology

Background:

Integrating and analyzing phenotype data across humans and model organisms is crucial for understanding biology and disease.
Diverse phenotypes and anatomical details across species pose challenges for cross-species and cross-phenotype class matching.
PhenomeNET was developed for disease gene prioritization, featuring an ontology to integrate phenotype ontologies.

Purpose of the Study:

To apply PhenomeNET for identifying related classes across multiple phenotype and disease ontologies.
To demonstrate the utility of automated reasoning in ontology alignment.
To enhance the integration of cross-species phenotype data for biomedical research.

Main Methods:

Application of the PhenomeNET system to phenotype and disease ontologies.
Utilizing automated reasoning to identify mappings between ontology classes.
Combining automated reasoning with lexical matching techniques.

Main Results:

Successfully identified a significant number of mappings between ontology classes.
Demonstrated that automated reasoning can uncover complex relationships not apparent through lexical methods alone.
Showcased improved ontology alignment by integrating automated reasoning with lexical matching.

Conclusions:

PhenomeNET effectively aligns and integrates phenotype ontologies.
The system facilitates biomedical analyses by connecting model organism observations to human disease genes and mutations.
Enables leveraging model organism research for understanding human pathophysiology.