DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Herrero-Zazo, María; Segura-Bedmar, Isabel; Hastings, Janna; Martı́nez, Paloma

doi:10.1021/acs.jcim.5b00119

Cited by 38 publications

(32 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It has been applied for multiple purposes, including in support of the bioinformatics and systems biology of metabolism [14], biological data interpretation [15,16], natural language processing [17], and as a chemistry component for the semantic web (e.g. [18,19]). However, each entity in ChEBI is manually classified, which creates an obvious bottleneck that hinders the utility of ChEBI and its chemical classification.…”

Section: Chemical Ontologiesmentioning

confidence: 99%

Learning Chemistry: Exploring the Suitability of Machine Learning for the Task of Structure-based Chemical Ontology Classification

Hastings

Glauer

Memariani

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Chemical data is increasingly openly available in databases such as PubChem, which contains more than 110 million compound entries as of October 2020. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory articial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We nd that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.

show abstract

Section: Chemical Ontologiesmentioning

confidence: 99%

Learning Chemistry: Exploring the Suitability of Machine Learning for the Task of Structure-based Chemical Ontology Classification

Hastings

Glauer

Memariani

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to using description logicbased rules in OWL, work has also been undertaken to use SWRL (Semantic Web Rule Language). Herreo-Zao et al [90] created DINTO (Drug-Drug Interactions Technology) to formally represent different types of drug-drug interactions (DDIs) using SWRL rules to infer different types of DDIs. By using this rule-based inferencing technique, the researchers were able to demonstrate that DDIs and their mechanisms could be inferred on a much larger scale than with previous knowledge bases.…”

Section: Semantic Applications In Drug Discoverymentioning

confidence: 99%

A new wave of innovation in Semantic web tools for drug discovery

Kanza

Frey

2019

Expert Opinion on Drug Discovery

View full text Add to dashboard Cite

Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases). Areas covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage. Expert opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.

show abstract

“…Decidability can be regained by restricting the form of admissible rules, by imposing a suitable safety condition [24]. Being supported by the Protégé ontology editor [25] as well as by popular rule engines and ontology reasoners, such as Jess [7], Drools [5] and Pellet [27], SWRL has become a very popular choice for developing rule-based applications on top of ontologies [3,12,22,28]. However, SWRL being around for more than 10 years now, it is most probable that it will never become a W3C standard; therefore, its scope is difficult to reach out to the industrial world.…”

Section: Introductionmentioning

confidence: 99%

SWRL2SPIN: Converting SWRL to SPIN

Bassiliades

2018

EasyChair Preprints

View full text Add to dashboard Cite

SWRL is a semantic web rule language that combines OWL ontologies with Horn Logic rules of the RuleML family of rule languages. Being supported by Protégé as well as by popular rule engines and ontology reasoners, such as Jess, Drools and Pellet, SWRL has become a very popular choice for developing rule-based applications on top of ontologies. However, being doubtful whether SWRL will become a W3C standard, it is difficult to reach out to the industrial world. On the other hand, SPIN has become a de-facto industry standard to represent SPARQL rules and constraints on Semantic Web models, building on the widespread acceptance of the SPARQL query language. In this paper, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by being transformed into SPIN. To this end, we have developed a prototype tool using SWI-Prolog that takes as input an OWL ontology with a SWRL rule base and transforms SWRL rules into SPIN rules in the same ontology, taking into consideration the object-oriented scent of SPIN, i.e. linking rules to the appropriate ontology classes as derived by analyzing the rule conditions.

show abstract

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Cited by 38 publications

References 49 publications

Learning Chemistry: Exploring the Suitability of Machine Learning for the Task of Structure-based Chemical Ontology Classification

Learning Chemistry: Exploring the Suitability of Machine Learning for the Task of Structure-based Chemical Ontology Classification

A new wave of innovation in Semantic web tools for drug discovery

SWRL2SPIN: Converting SWRL to SPIN

Contact Info

Product

Resources

About