A 2018 workshop: vaccine and drug ontology studies (VDOS 2018)

Hur, Junguk; Tao, Cui; He, Yongqun

doi:10.1186/s12859-019-3191-9

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…If it is found these drugs has to be changed to prevent future ADRs. In [7], authors have developed a machine learning and rulebased model for predicting Adverse Drug Reactions (ADR) by considering the drug labels. Authors have used Medical Directory for Regulatory Activities (MedRA) for normalization.…”

Section: B B Adverse Drug Reactions (Adr)mentioning

confidence: 99%

An Efficient Ontology Based Drug Prescription Model

Gulzar,

Ahmed

2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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Medication is a process of prescribing medicines by knowledgeable physicians. Medicines which are not prescribed when consumed may generate side effects. Some diseases require more than one drug to control the disease. If drugs are not carefully prescribed adverse reactions may happen. Meny people died due to medical errors in prescribing medicines by medical practitioners based on their experience. For avoiding all these adverse effects, we need a recommendation system or a decision support system for efficiently prescribing medicines. Many parameters need to be considered before prescribing the medicines like patient's age, medical history, side effects of drugs, possible allergies, drug-drug interactions, drug-disease interactions and drug-food interactions. Semantic web provides tools and technologies like ontologies to construct recommendation models and can retrieve data using tools like SPARQL and inference mechanisms to infer new knowledge.

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Section: B B Adverse Drug Reactions (Adr)mentioning

confidence: 99%

An Efficient Ontology Based Drug Prescription Model

Gulzar,

Ahmed

2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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“…Our studies demonstrate that VO can be used to support data integration and advanced data analysis. Our previous VO research efforts have been focused on the usage of VO for representing vaccines against infectious diseases [31][32][33]. With the ongoing effort of CanVaxKB development, we have also used the VO to represent cancer vaccines [20].…”

Section: Introductionmentioning

confidence: 99%

Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis

Zheng,

Li,

Masci

et al. 2024

J Biomed Semant

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Background The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured relationships and standardized definitions found in an ontology. Recognizing this, we expanded the Vaccine Ontology (VO) to include those cancer vaccines present in CanVaxKB that were not initially covered, enhancing VO’s capacity to systematically define and interrelate cancer vaccines. Results An ontology design pattern (ODP) was first developed and applied to semantically represent various cancer vaccines, capturing their associated entities and relations. By applying the ODP, we generated a cancer vaccine template in a tabular format and converted it into the RDF/OWL format for generation of cancer vaccine terms in the VO. ‘12MP vaccine’ was used as an example of cancer vaccines to demonstrate the application of the ODP. VO also reuses reference ontology terms to represent entities such as cancer diseases and vaccine hosts. Description Logic (DL) and SPARQL query scripts were developed and used to query for cancer vaccines based on different vaccine’s features and to demonstrate the versatility of the VO representation. Additionally, ontological modeling was applied to illustrate cancer vaccine related concepts and studies for in-depth cancer vaccine analysis. A cancer vaccine-specific VO view, referred to as “CVO,” was generated, and it contains 928 classes including 704 cancer vaccines. The CVO OWL file is publicly available on: http://purl.obolibrary.org/obo/vo/cvo.owl, for sharing and applications. Conclusion To facilitate the standardization, integration, and analysis of cancer vaccine data, we expanded the Vaccine Ontology (VO) to systematically model and represent cancer vaccines. We also developed a pipeline to automate the inclusion of cancer vaccines and associated terms in the VO. This not only enriches the data’s standardization and integration, but also leverages ontological modeling to deepen the analysis of cancer vaccine information, maximizing benefits for researchers and clinicians. Availability The VO-cancer GitHub website is: https://github.com/vaccineontology/VO/tree/master/CVO.

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Construction of the recombinant Escherichia coli strain producing a homologous thermolabile toxin subunit

et al. 2021

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Colibacteriosis is an acute zoonotic disease manifested by septicaemia, toxemia, enteritis, body dehydration, and central nervous system damage. Depending on the presence of virulence factors and the nature of interaction with the intestinal mucosa, enterotoxigenic, enteroinvasive, enteropathogenic, and enterohemorrhagic E. coli are isolated. Enterotoxigenic strains of E. coli occupy one of the leading places in the etiological structure of calf colibacteriosis in many livestock farms of the Republic of Belarus. The main reason why this disease develops is the presence of thermolabile and thermostable toxins in the causative strain. The thermolabile toxin subunit B is a potent antigen that allows pet immunity to be acquired against E. coli-induced cattle diarrhea. Many foreign vaccines used against intestinal infections of cattle contain either a native or recombinant variant of the subunit B. As a result of the work, we have created a new strain of E. coli 42eLTB – the producer of the recombinant subunit B of the thermolabile toxin E. coli. The producing capacity of the obtained strain is 480 mg with 1 culture liquid liter, which exceeds the already known strains 1.37 times.

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A 2018 workshop: vaccine and drug ontology studies (VDOS 2018)

Cited by 3 publications

References 49 publications

An Efficient Ontology Based Drug Prescription Model

An Efficient Ontology Based Drug Prescription Model

Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis

Construction of the recombinant Escherichia coli strain producing a homologous thermolabile toxin subunit

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