ArticleJournal of biomedical semantics2017
Drug target ontology to classify and integrate drug discovery data.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 75 citations in OpenAlex.
- Decoding the dark genome reveals its organisation into modular disease networks.Scientific reports · 2026Article
- Integrative Network Analysis of Single-Cell RNA Findings and a Priori Knowledge Highlights Gene Regulators in Multiple Myeloma Progression.International journal of molecular sciences · 2026Article
- The Immunopeptidomics Ontology (ImPO).Database : the journal of biological databases and curation · 2024Article
- Kinome-Wide Virtual Screening by Multi-Task Deep Learning.International journal of molecular sciences · 2024Article
- FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.Bioinformatics (Oxford, England) · 2024Article
- Exploring DrugCentral: from molecular structures to clinical effects.Journal of computer-aided molecular design · 2023Review
- Autophagy dark genes: Can we find them with machine learning?Natural sciences (Weinheim, Germany) · 2023Article
- Article
- Article
- Genomic and proteomic biomarker landscape in clinical trials.Computational and structural biotechnology journal · 2023Article
- AlphaFold illuminates half of the dark human proteins.Current opinion in structural biology · 2022Review
- Systematic illumination of druggable genes in cancer genomes.Cell reports · 2022Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.Frontiers in genetics · 2022Article
- Getting Started with the IDG KMC Datasets and Tools.Current protocols · 2022Article
- TIGA: target illumination GWAS analytics.Bioinformatics (Oxford, England) · 2021Article
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Targeting the ion channel TRPM7 promotes the thymic development of regulatory T cells by promoting IL-2 signaling.Science signaling · 2020Article
- Recent progress on cheminformatics approaches to epigenetic drug discovery.Drug discovery today · 2020Review
- Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma.JMIR medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.
resultsAs part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.
conclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.