![Ontology spectrum by Jimeno et. al [1]
[1]: Antonio Jimeno-Yepes, Ernesto Jim´enez-Ruiz, Rafael Berlanga, and Dietrich Rebholz-Schuhmann. Use of shared lexical resources for efficient
ontological engineering. In Semantic Web Applications and Tools for Life Sciences Workshop (SWAT4LS). CEUR WS Proceedings, volume 435, pages 93–
136, 2008
9](https://image.slidesharecdn.com/quantifyingthecontentofbiomedicalsemanticresourcesasacorefordrugdiscoveryplatforms-170606204118/85/Quantifying-the-content-of-biomedical-semantic-resources-as-a-core-for-drug-discovery-platforms-9-320.jpg)
![Triples vs. Unique Entities (selected LS-LOD datasets)
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86942371
6679943
2380009
188380
19920395
2199594
409942525
50061452
98835804
7337123
326720894
19768641
8801487
530538
3672531
316950
73048
6995
11663
1129
97520151
5950074
3628205
372136
7189769
869985
2323345
176579
3306107223
364255265
48781511
3110993
50197150
6533307
2174579
59776
55914
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# OF TRIPLES # OF UNIQUE ENTITIES
[affymetrix] [biomodels] [bioportal] [chembl] [clinicaltrials] [ctd] [dbsnp] [drugbank] [genage] [gendr]
[goa] [hgnc] [homologene] [interpro] [iproclass] [irefindex] [kegg] [linkedspl] [lsr] [mesh]](https://image.slidesharecdn.com/quantifyingthecontentofbiomedicalsemanticresourcesasacorefordrugdiscoveryplatforms-170606204118/85/Quantifying-the-content-of-biomedical-semantic-resources-as-a-core-for-drug-discovery-platforms-24-320.jpg)
![[2]: Zeginis, D., et al.: A collaborative methodology for developing a semantic model for interlinking Cancer
Chemoprevention linked-data sources. Semantic Web (2013)
[3]: Hasnain, A.’ et al.: Linked Biomedical Dataspace: Lessons Learned integrating Data for Drug Discovery. In:
International Semantic Web Conference (In-Use Track), October 2014 (2014)
Related Work (selected)
• Zeginis et al. [2] proposed “meet-in-the-middle” approach to develop
the semantic model relevant for cancer chemoprevention. Relevant
data was analysed in a bottom-up fashion from analysing the
domain whereas a top-down approach was considered to collect
ontologies, vocabularies and data models.
• Hasnain et al. [3] proposed Linked Biomedical Dataspace (access
and use biomedical resources relevant for cancer chemoprevention)
with components namely:
– a) knowledge extraction,
– b) link creation,
– c) query execution and
– d) knowledge publishing.
25](https://image.slidesharecdn.com/quantifyingthecontentofbiomedicalsemanticresourcesasacorefordrugdiscoveryplatforms-170606204118/85/Quantifying-the-content-of-biomedical-semantic-resources-as-a-core-for-drug-discovery-platforms-25-320.jpg)
Quantifying the content of biomedical semantic resources as a core for drug discovery platforms
- 1. Quantifying the content of Biomedical Semantic Resources as a core for Drug Discovery Platforms Ali Hasnain and Dietrich Rebholz-Schuhmann May 2017
- 2. Agenda • Introduction • Motivation • Ontologies • Biomedical Ontologies • Drugs and Chemical Compound Ontologies • Upper level Ontologies • Data Repositories/ Databases for Drug Discovery • Gene, Gene Expression and Protein Databases • Pathway databases • Chemical and Structure Databases • Disease Specific Databases for Prevention • Literature databases • Life Sciences Linked Open Data Cloud • Linked Open Drug Data (LODD) • Bio2RDF • LinkedLifeData • Related Work • Conclusion 2
- 3. Introduction • Biomedical data exists as ontologies, repositories, and other open data resources e.g, Life Science Linked Open Data (LS- LOD) relevant in the context of Drug Discovery and Cancer Chemoprevention. • The analysis gives an overview of which resources have to be considered, what amount of data requires integration and provides the opportunity to tailor semantic solutions to specific needs in terms of size and performance.
- 4. We live in a world of data Motivation 4
- 5. Linked Data for Cancer Chemoprevention • Because Biomedical Data is heterogeneous and spread across multiple sources 5 ~5 molecs testable in the lab ~2000 small molecs ~100 molecs ~ 10 interesting pathways Literature Insilicomodels Browsedatabases Hypothesis Generation Linked Data
- 6. Heterogeneous Data – Multiple Data sources DrugBank DailyMed CheBI, KEGG Reactome Sider BioPax Medicare 6
- 7. Biomedical Data Integration nih:EGFR epidermal growth factor receptor Homo sapiens CCCCGGCGCAGCGCGGCCGCAGCA GCCTCCGCCCCCCGCACGGTGTGA GCGCCCGACGCGGCCGAGGCGG … nih:EGF nci:has_description nih:sequence nih:organism nih:interacts nih:organism rea:EGFR rea:Membrane rea:Receptor rea:Transferase rea:keyword rea:keyword rea:keyword NCBI Reactome sameAs 7
- 8. Ontologies These ontologies can fall into three main categories: 1. The Biomedical ontologies are mainly used by biomedical applications and define the basic biological structures (e.g. genes, pathways etc). 2. The Drugs and Chemical Compound Ontologies are related to the clinical drugs and their active ingredients. 3. The upper level ontologies describe general concepts that many biomedical ontologies share. 8
- 9. Ontology spectrum by Jimeno et. al [1] [1]: Antonio Jimeno-Yepes, Ernesto Jim´enez-Ruiz, Rafael Berlanga, and Dietrich Rebholz-Schuhmann. Use of shared lexical resources for efficient ontological engineering. In Semantic Web Applications and Tools for Life Sciences Workshop (SWAT4LS). CEUR WS Proceedings, volume 435, pages 93– 136, 2008 9
- 10. Biomedical Ontologies (selected) • Advancing Clinico-Genomic Trials on Cancer (ACGT) Master Ontology (MO) – data exchange in oncology, integration of clinical and molecular data • Biological Pathway Exchange (BioPAX) – metabolic, biochemical, transcription regulation, protein synthesis, signal transduction pathways • Experimental Factor Ontology (EFO) – enhance and promote consistent annotation, automatic annotation to integrate external data • Gene Ontology (GO) – for describing biological processes, molecular functions and cellular components of gene products • Medical Subject Headings (MeSH) – hierarchical structure for indexing, cataloguing, and searching for biomedical/ health-related data. • Microarray Gene Expression Data Ontology (MGED) – the biological sample, the treatment sample and the micro-array chip technology in the experiment • National Cancer Institute (NCI) Thesaurus – integrates molecular and clinical cancer-related information to integrate, retrieve and relate concepts • Ontology for biomedical Investigations (OBI) – designs, protocols, instrumentation, materials, processes, data in biological & biomedical investigations 10
- 11. Drugs and Chemical Compound Ontologies (selected) • RxNorm – standard names for clinical drugs active drug ingredient, dosage strength, physical form) and links • Basic Formal Ontology (BFO) – formalise entities such as 3D enduring objects and comprehending processes • OBO Relation Ontology (RO) – formal definitions of basic relations that cross-cut the biomedical domain • Provenance Ontology (PROVO) – provides classes, properties and restrictions for provenance information 11 Generic and Upper Ontologies (selected)
- 12. Statistical overview of implementation details of Ontologies (selected) Ontology Category Year* Topic Implementation Classes Properties Individuals Depth ACGT-MO Biomedical 2008 Cancer OWL/CVC/RDF/XML 1769 260 61 18 BioPAX Biomedical 2010 Pathways OWL/CVC/RDF/XML 68 96 0 4 EFO Biomedical 2015 Experimental Factors OWL/CVC/RDF/XML 18596 35 0 14 GO Biomedical 2016 Genomics and Proteomic OWL/CVC/RDF/XML 4419 9 0 16 MeSH Biomedical 2009 Health RDF/TTL/ CSV 252375 38 0 15 MGED Biomedical 2009 Microarray Experiment OWL/CVC/RDF/XML 233 121 698 8 NCIT Biomedical 2007 Clinical care OWL/CVC/RDF/XML 118167 173 45715 16 OBI Biomedical 2008 Experimental Data OWL/CVC/RDF/XML 2932 106 178 16 UMLS Biomedical 1993 Biomedical/ Health RDF 3221702 - - - RxNorm Drugs 1993 Clinical Drugs OWL/CVC/RDF/XML 118555 46 0 0 BFO Generic 2003 Genuine Upper Ontology OWL/CVC/RDF/XML 35 0 0 5 RO Generic 2005 Relations used in all OBO ontologies OWL/CVC/RDF/XML - - - - PROVO Generic 2012 PROV Data Model OWL/CVC/RDF/XML 30 50 4 3 *Statistics as of Aug 2016 - listed at BioPortal- Year specify the time when the last- most recent version is produce. “-” means information not available. 12
- 13. Classes vs. Properties plot (Selected Ontologies) 13 1769 260 68 96 18596 35 4419 9 252375 38 233 121 118167 173 2932 106 3221702 118555 4635 30 50 CLASSES PROPERTIES ACGT-MO BioPAX EFO GO MeSH MGED NCIT OBI UMLS RxNorm BFO RO PROVO
- 14. Public Data Repositories for Drug Discovery • The databases are separated into the following categories: – Gene, Gene Expression and Protein Databases for gene and protein annotations as well as the expression levels and related clinical data. – Pathway Databases denoting the protein interactions and the overall functional outcomes. – Chemical and Structure Databases including Biological Activities for the information related to drugs and other chemicals including also toxicity observations and clinical trials. – Disease Specific Databases for Prevention which deliver content specific to the prevention of cancer. – Literature Databases 14
- 15. Gene, Gene Expression and Protein Databases • GenBank – over 65 B nucleotide bases in more than 61 M sequences • ArrayExpress – 65060 experiments 1'973'776 assays, annotated data for gene expression from biological experiments • Gene Expression Omnibus (GEO) – 3'848 datasets gene expression for specific studies • Universal Protein Resource (UniProt) – 63'686'057 sequences, 21'364'768'379 amino acids classifications, cross-references, annotation of proteins • Protein Data Bank (PDB) – 118280 Biological Structures evidence of experimentally validated protein structures • Protein Database – 30'047Protein Entries, 41'327PPIs translated coding regions from GenBank, TPA, SwissProt, PIR, PRF, UniProt and PDB. 15
- 16. Pathway Databases • Kyoto Encyclopedia of Genes and Genomes (KEGG) – 432'883PathwayMaps, 153'776hierarchies, genome sequencing and high-throughput experimental technologies • Reactome – 9'386 Proteins and pathway data for signalling, transcriptional regulation, translation, apoptosis, other • Wikipathways – 2'475 pathways complementing e g. KEGG, Reactome, Pathway Commons • cPath: Pathway Database Software – 31'698 pathways, 1'151'476 interactions, pathway visualisation, analysis and modelling 16
- 17. Chemical and Structure Databases including Biological Activities • Chemical Compounds Database (Chembase) – 150'000 pages, compounds, their physical and chemical properties, mass spectra • Chemical Entities of Biological Interest (ChEBI) – 48'296 compounds, natural and synthetic atom, molecule, ion, radical, conformer • DrugBank – 8,261 drugs, 4,164 targets, 243 Enzymes, 118 Transporters, drug (chemical, pharmaceutical), drug target (sequence, structure, pathway) • PubChem – 89'124'401 Compounds, compound neighbouring, sub/superstructure, bioactivity data • Aggregated Computational Toxicology Resource (ACToR) – more than 500 public source , environmental chemicals searchable by name and structure • ClinicalTrials – 213'868 studies , offers information for locating clinical trials for diseases and conditions • TOXicology Data NETwork (TOXNET) – toxicology, hazardous chemicals, environmental health and related areas 17
- 18. Disease Specific Databases for Prevention• Colon Chemoprevention Agents Database (CCAD) – 1,137 agents and literature data for colon chemoprevention in human, rats, mice • Dietary Supplements Labels Database – 5'000 brands of dietary supplements to compare label ingredients in different brands. Links to other databases such as MedlinePlus and PubMed • REPAIRtoire Database – DNA damage links, pathways, proteins for DNA re-pair, diseases related to mutations • Pubmed – journal citations i.e. Primary source of information for bio-medical researchers • PubMed Dietary Supplement Subset – dietary supplement literature including vitamin, mineral, botanical/herbal supplements 18 Literature Databases
- 19. Statistical overview of implementation details of libraries and databases (selected) Database Category Year* Topic Implementation Size/ Stats PubMed Literature 1996 Biomedical Literature WebBased/ CSV 11 M Journal citations PDSS Literature 1999 Citations of dietary supplement WebBased X DSLD Chemoprevention 2013 Ingredients of dietary supplement WebBased > 5000 selected brands ClinicalTrials Toxicity 2000 Clinical Trials WebBased 213,868 studies TOXNET Toxicity 1987 Toxicology Database WebBased X ACToR Compound 2008 Chemical Toxicity Data WebBased >500 public sources DrugBank Compound 2008 Drug Data WebBased/LOD 8206 drugs ChEBI Compound X Small Molecular entities WebBased/LOD 48,296 compounds PubChem Compound 2004 Compound Structure WebBased/LOD 89,124,401 compounds ChemSpider Chemical 2007 Compound Structure WebBased >40 million structures KEGG Pathway 1995 Genomic, Chemical, systemic WebBased/LOD 432883pathway maps Reactome Pathway 2003 Pathways WebBased 9386 proteins Wikipathway Pathway 2007 Biological pathways WebBased 2475 pathways cPath Pathway 2005 Biological pathways Desktop/WebBased 31698 pathways Uniprot Protein 2002 Protein Sequence WebBased/LOD 63686057sequences PDB Protein 1971 3D structural data of Proteins WebBased/LOD 30,047protein *Statistics as of Aug 2016 - Year specify the time when the last- most recent version is produce. “X” means information not available. 19
- 20. Life Sciences Linked Open Data Cloud • Linked biomedical datasets relevant in a Cancer Chemoprevention and drug discovery scenario: – Linked Open Drug Data (LODD) • Set of linked datasets relevant to Drug Discovery that includes data from several datasets including Drugbank, LinkedCT, DailyMed, Diseasome, SIDER, STITCH, Medicare, RxNorm, ClinicalTrials.gov, NCBI Entrez Gene and OMIM. – Bio2RDF • Contains multiple linked biological databases including pathways databases such as KEGG, PDB and several NCBIs databases. An open-source project that uses Semantic Web technologies to build and provide the largest network of Linked Data for the Life Sciences. – LinkedLifeData • A semantic data integration platform for the biomedical domain containing 5 billion RDF statements from various sources including UniProt, PubMed, EntrezGene and 20 more. 20
- 21. The Linked Open Data Cloud “Life sciences will drive adoption of the Semantic Web, just as high-energy physics drove the early Web.” - Sir Tim Berners-Lee, 2005 Proteins Molecules Genes Diseases 21
- 22. Meaningful Biomedical Correlation Proteins Molecules Genes Diseases :Protein :Molecule :Gene :Disease Uniprot PDB Pfam PROSITE ProDom Uniref UniPark Daily medDrug Bank ChemBL Pub Chem KEGG Gene Ontology GeneID Affy metrix Homo gene MGI Disea some SIDER 22
- 23. Statistical overview of datasets involved in LS- LOD, Bio2RDF and LLD (selected) Dataset Category Year* Topic Size/ Coverage Drugbank LODD 2010 Drugs 766920 triples, 4800 drugs LinkedCT LODD X Clinical Trials 25 M triples, 106000 trials DailyMed LODD 2010 Drugs 1604983 triples, >36K products Dbpedia LODD 2009 Drugs/ Diseases/Proteins 218M triples, 2300 drugs, 2200 proteins Diseasome LODD 2010 Diseases/ Genes 91182 triples, 2600 genes SIDER LODD 2010 Diseases/ Side Effects 192515 triples, 63K effects, 1737 genes STITCH LODD 2010 Chemicals/ Proteins 7.5 M chemicals, 0.5 M proteins ChEMBLE LODD 2010 Assay/ Proteins/ Organisms 130 M triples Affymetrix Bio2RDF 2014 Microarrays 8694237 triples, 6679943 entities BioModels Bio2RDF 2014 Biological/ mathematical models 2380009 triples, 188308 entities BioPortal Bio2RDF 2014 Biological/ biomedical entities 19920395 triples, 2199594 entities KEGG Bio2RDF 2014 Genes 50197150 triples, 6533307 entities PharmaG-KB Bio2RDF 2014 Genotypes/ Phenotypes 278049209 triples, 25325504 entities PubMed Bio2RDF 2014 Citations 5005343905 triples, 412593720 entities Taxonomy Bio2RDF 2014 Taxonomy 21310356 triples, 1147211 entities LLD LLD 2014 Drugs, Chromosomes 10192641644 statements *Statistics as of Aug 2016 (source DataHub) - Year specify the time when the last- most recent version is produced. “X” means information not available. 23
- 24. Triples vs. Unique Entities (selected LS-LOD datasets) 24 86942371 6679943 2380009 188380 19920395 2199594 409942525 50061452 98835804 7337123 326720894 19768641 8801487 530538 3672531 316950 73048 6995 11663 1129 97520151 5950074 3628205 372136 7189769 869985 2323345 176579 3306107223 364255265 48781511 3110993 50197150 6533307 2174579 59776 55914 5032 7323864 305401 # OF TRIPLES # OF UNIQUE ENTITIES [affymetrix] [biomodels] [bioportal] [chembl] [clinicaltrials] [ctd] [dbsnp] [drugbank] [genage] [gendr] [goa] [hgnc] [homologene] [interpro] [iproclass] [irefindex] [kegg] [linkedspl] [lsr] [mesh]
- 25. [2]: Zeginis, D., et al.: A collaborative methodology for developing a semantic model for interlinking Cancer Chemoprevention linked-data sources. Semantic Web (2013) [3]: Hasnain, A.’ et al.: Linked Biomedical Dataspace: Lessons Learned integrating Data for Drug Discovery. In: International Semantic Web Conference (In-Use Track), October 2014 (2014) Related Work (selected) • Zeginis et al. [2] proposed “meet-in-the-middle” approach to develop the semantic model relevant for cancer chemoprevention. Relevant data was analysed in a bottom-up fashion from analysing the domain whereas a top-down approach was considered to collect ontologies, vocabularies and data models. • Hasnain et al. [3] proposed Linked Biomedical Dataspace (access and use biomedical resources relevant for cancer chemoprevention) with components namely: – a) knowledge extraction, – b) link creation, – c) query execution and – d) knowledge publishing. 25
- 26. Conclusion • In this paper we introduce and classify different tiers of biomedical Data relevant to Cancer Chemoprevention and Drug Discovery domain. • This involves Ontologies, databases and Life Science Linked Open Data in Healthcare, Life Sciences and Biomedical Domain • We classify ontologies into three main classes: – i) biomedical Ontologies (e.g. EFO, OBI, GO etc), – ii) Drugs and Chemical Compound Ontologies (e.g. RxNorm) and – iii) Generic and Upper Ontologies (e.g. BFO, RO, PROV). • Similarly we categorise libraries and databases in five categories: – (i) Gene, Gene Expression and Protein Databases, – (ii) Pathway databases, – (iii) Chemical and Structure Databases including Biological Activities, – (iv) Disease Specific Databases for Prevention, and – (v) Literature databases. 26
- 27. Thank you
Editor's Notes
- #5: We live in a world of data!
- #6: Link to next slide is –Linked data is the faclitates complex queries and workflows to be assembled
- #7: To discovery which links could our datasets have to other datasources, we’ve explored what types of data are published in the linked open data cloud. What we found was a lot of messy data – looking at 8 datasets containing molecular data, their descriptions are very different; chebi calls molecules compounds, drugbank calls them drugs, dailymed calls them drugs as well but uses a different identifier. Link – how to start linking all of these datasets such that they can be made available in a unified query interface?
- #8: EGFR: Epidermal growth factor receptor
- #13: BioMedical Ontologies: Advancing Clinico-Genomic Trials on Cancer (ACGT) Master Ontology (MO Biological Pathway Exchange (BioPAX) Experimental Factor Ontology (EFO) Gene Ontology (GO Medical Subject Headings (MeSH Microarray Gene Expression Data Ontology (MGED National Cancer Institute (NCI) Ontology for biomedical Investigations (OBI Unified Medical Language System (UMLS) Drugs and Chemical Compound Ontologies: RxNorm Generic and Upper Ontologies: Basic Formal Ontology (BFO OBO Relation Ontology (RO) Provenance Ontology (PROVO)
- #20: Literature Databases: Pubmed PubMed Dietary Supplement Subset Natural Sources of Chemoprevention Agents Databases: Dietary Supplements Labels Database Toxicity and Efficacy Databases: ClinicalTrials TOXicology Data NETwork (TOXNET) Biological Activity of Compounds Databases Aggregated Computational Toxicology Resource (ACToR) DrugBank Chemical Entities of Biological Interest (ChEBI) PubChem Repartoire Database Gene Expression Databases: Cancer Gene Expression Database (CGED) ArrayExpress Gene Expression Omnibus (GEO) Gene and DNA Databases: GenBank Chemical and Physical Structure Databases: ChemSpider Chemical Compounds Database (Chembase) Sigma-Aldrich ChemDB Disease Specific Compound Databases: Colon Chemoprevention Agents Database Pathway Databases: Kyoto Encyclopedia of Genes and Genomes (KEGG) Reactome Wikipathways\footnote cPath: Pathway Database Software\footnote Protein Databases: Universal Protein Resource (UniProt) Protein Data Bank (PDB) Protein Database
- #23: M: when data is catalogues, we can discovering new links by crossreferencing with existing datasets -> once we identify these concepts, how do we actualy query them toegether?