ArticlePLoS computational biology2010
Semantic similarity for automatic classification of chemical compounds.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- SeEn: Sequential enriched datasets for sequence-aware recommendations.Scientific data · 2022Article
- Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.iScience · 2021Review
- Hybrid semantic recommender system for chemical compounds in large-scale datasets.Journal of cheminformatics · 2021Article
- Multi-domain semantic similarity in biomedical research.BMC bioinformatics · 2019Article
- Representing antibiotic relationships using measurements of efficacy against clinical isolates.Wellcome open research · 2019Article
- STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.Frontiers in bioengineering and biotechnology · 2019Article
- Machine learning in chemoinformatics and drug discovery.Drug discovery today · 2018Review
- Inferring ontology graph structures using OWL reasoning.BMC bioinformatics · 2018Article
- Evaluating the effect of annotation size on measures of semantic similarity.Journal of biomedical semantics · 2017Article
- The role of ontologies in biological and biomedical research: a functional perspective.Briefings in bioinformatics · 2015Article
- Article
- A rule-based ontological framework for the classification of molecules.Journal of biomedical semantics · 2014Article
- Thematic series on biomedical ontologies in JBMS: challenges and new directions.Journal of biomedical semantics · 2014Review
- Enhancement of chemical entity identification in text using semantic similarity validation.PloS one · 2013Article
- The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013.Nucleic acids research · 2013Article
- Structure-based classification and ontology in chemistry.Journal of cheminformatics · 2012Article
- Chemical Entity Recognition and Resolution to ChEBI.ISRN bioinformatics · 2012Article
- Assessing drug target association using semantic linked data.PLoS computational biology · 2012Article
- Disjunctive shared information between ontology concepts: application to Gene Ontology.Journal of biomedical semantics · 2011Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.