Semantic Technologies in Drug Discovery

Kanza, Samantha; Frey, Jeremy G.

doi:10.1016/b978-0-12-801238-3.11520-x

Cited by 4 publications

(2 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, disaster knowledge has moderate interoperability and reusability. Ontology findability, access [105] and reuse in the biomedical domain [106,107] are slightly higher than in the disaster domain because of ontologies are archived using established design principles (e.g., the OBO foundry principles) in dedicated semantic repositories. The ability to share and discover knowledge using a coordinated approach towards semantics will guarantee the reuse of evolving disaster management knowledge and data interoperability.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review

Mazimwe

Hammouda

Gidudu

2021

IJGI

View full text Add to dashboard Cite

The success of disaster management efforts demands meaningful integration of data that is geographically dispersed and owned by stakeholders in various sectors. However, the difficulty in finding, accessing and reusing interoperable vocabularies to organise disaster management data creates a challenge for collaboration among stakeholders in the disaster management cycle on data integration tasks. Thus the need to implement FAIR principles that describe the desired features ontologies should possess to maximize sharing and reuse by humans and machines. In this review, we explore the extent to which sharing and reuse of disaster management knowledge in the domain is inline with FAIR recommendations. We achieve this through a systematic search and review of publications in the disaster management domain based on a predefined inclusion and exclusion criteria. We then extract social-technical features in selected studies and evaluate retrieved ontologies against the FAIR maturity model for semantic artefacts. Results reveal that low numbers of ontologies representing disaster management knowledge are resolvable via URIs. Moreover, 90.9% of URIs to the downloadable disaster management ontology artefacts do not conform to the principle of uniqueness and persistence. Also, only 1.4% of all retrieved ontologies are published in semantic repositories and 84.1% are not published at all because there are no repositories dedicated to archiving disaster domain knowledge. Therefore, there exists a very low level of Findability (1.8%) or Accessibility (5.8%), while Interoperability and Reusability are moderate (49.1% and 30.2 % respectively). The low adherence of disaster vocabularies to FAIR Principles poses a challenge to disaster data integration tasks because of the limited ability to reuse previous knowledge during disaster management phases. By using FAIR indicators to evaluate the maturity in sharing, discovery and integration of disaster management ontologies, we reveal potential research opportunities for managing reusable and evolving knowledge in the disaster community.

show abstract

Section: Discussion Of Resultsmentioning

confidence: 99%

Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review

Mazimwe

Hammouda

Gidudu

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…75,76 Many others exist, Strömert et al produced a comprehensive review of ontologies in Chemistry, 77 and many others can be viewed on BioPortal 78 (such as the Gene Ontology 79,80 or the BioAssay Ontology 81,82 ) and other ontology repositories. In order to identify the usefulness and relevance of different ontologies an evaluation would need to be performed, much like Kanza and Frey 83 undertook in the domain of Drug Discovery ontologies.…”

Section: Technologies and Softwarementioning

confidence: 99%

Digital research environments: a requirements analysis

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

KGML-xDTD: a knowledge graph–based machine learning framework for drug treatment prediction and mechanism description

Ma,

Zhou,

Liu

et al. 2022

GigaScience

View full text Add to dashboard Cite

Background Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings. Results In this work, we propose KGML-xDTD: a Knowledge Graph–based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a 2-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable MOAs. We leverage knowledge-and-publication–based information to extract biologically meaningful “demonstration paths” as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths. Conclusions KGML-xDTD is the first model framework that can offer KG path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce “black-box” concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations and further accelerate the process of drug discovery for emerging diseases.

show abstract

Semantic Technologies in Drug Discovery

Cited by 4 publications

References 87 publications

Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review

Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review

Digital research environments: a requirements analysis

KGML-xDTD: a knowledge graph–based machine learning framework for drug treatment prediction and mechanism description

Contact Info

Product

Resources

About