Duc-Hau Le1,2. 1. Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam. 2. School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.
Abstract
BACKGROUND: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology. RESULTS: In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes. CONCLUSIONS: Taken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications. AVAILABILITY: UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use.
BACKGROUND: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology. RESULTS: In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes. CONCLUSIONS: Taken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications. AVAILABILITY: UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use.
Authors: Luke T Slater; Sophie Russell; Silver Makepeace; Alexander Carberry; Andreas Karwath; John A Williams; Hilary Fanning; Simon Ball; Robert Hoehndorf; Georgios V Gkoutos Journal: BMC Med Inform Decis Mak Date: 2022-02-05 Impact factor: 2.796
Authors: Mahima Vedi; Harika S Nalabolu; Chien-Wei Lin; Matthew J Hoffman; Jennifer R Smith; Kent Brodie; Jeffrey L De Pons; Wendy M Demos; Adam C Gibson; G Thomas Hayman; Morgan L Hill; Mary L Kaldunski; Logan Lamers; Stanley J F Laulederkind; Ketaki Thorat; Jyothi Thota; Monika Tutaj; Marek A Tutaj; Shur-Jen Wang; Stacy Zacher; Melinda R Dwinell; Anne E Kwitek Journal: Genetics Date: 2022-04-04 Impact factor: 4.562