Ontologies and Knowledge Graphs in Oncology Research
Abstract
:Simple Summary
Abstract
1. Introduction
2. Background
2.1. Ontologies
2.2. Ontologies in Cancer Research
3. Materials and Methods
3.1. Initial Search and Screening
3.2. Categorization
- Data Annotation: ontologies are used to describe data under a common schema, linking data objects to ontology classes that describe them.
- Data Integration: ontologies support the integration of different data sets or databases.
- Database Interface: ontologies are used to support user interfaces for databases, where labels of ontology classes and relations allow text annotation. These interfaces are notably useful in dealing with medical data, for integration and querying of different knowledge resources.
- NLP: ontologies are used as the vocabulary source for Natural Language Processing (NLP) methods, where entities, events or relations in a text are identified through the corresponding ontology labels.
- Reasoning: Automatic reasoners process ontologies’ axioms and their formal definitions.
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- Inference of New Knowledge: complex reasoning-based queries can reveal novel biological knowledge based on the already defined axioms.
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- Error Detection: reasoning applied to check for consistency (or contradictions) in the ontology.
- Data Mining and Analytics: ontologies are used to support data mining and analytics tasks.
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- Semantic Filtering: ontology-based annotations are used to filter and process data.
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- Semantic Similarity: ontology-based annotations are used to compare data entities.
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- Machine Learning: ontologies and KGs are explored by machine learning algorithms.
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- Gene Set Enrichment: statistical analysis of gene set ontology-annotations.
4. Ontologies in Oncology
4.1. Ontologies Used in the Reviewed Applications
4.2. Ontologies Created for the Reviewed Applications
Ref | Objective | Ontology Name | Domain | Reused Ontologies | Language |
---|---|---|---|---|---|
[51] | Model lung cancer for the clinical decision support application Lung Cancer Assistant | LUCADA ontology | Clinical | SNOMED-CT | OWL |
[33] | Use a hybrid approach to build a breast cancer ontology | N/A | Breast Cancer | N/A | OWL |
[32] | Describe cancer cells and capture the properties of tumorigenesis | OncoCL | Cell Lines | CL, UBERON, BTO, Pathway Ontology, PATO, CPO, SO | OWL |
[11] | Represent the project domain and link the NeoMark data to other domains | NeoMark ontology | Clinical | BFO, RO | OWL |
[50] | Cancer reclassification and drug inference | N/A | Farmacology | N/A | N/A |
[54] | Drug target prediction | CRC ontology | Colorectal Cancer | PharmGKB | OWL |
[63] | Assist medical students and professionals in the breast cancer domain | OntoMama | Clinical | N/A | N/A |
[34] | Development of an ontology-driven survivor engagement framework for mobile apps | POCS | Social | FOAF | OWL |
[46] | Creation of TNM-O | TNM-O | Anatomical | FMA, BioTopLite 2 | OWL |
[41] | Represent obesity-related cancer (ORC) ontology to organize information and allow data querying | FOORC | Obesity Related Cancer | DOID | OWL |
[49] | Extraction of association rules from large datasets on gastric cancer patients | Gastric cancer ontology | Clinical | N/A | N/A |
[55] | Aid data integration; enable association between SE variables and health outcomes | OCRSEV | Social-Ecological Factors | BFO | OWL |
[45] | Interoperability across quantitative histopathological imaging data sets | QHIO | Imaging | OBI | OWL |
[39] | Design of a semantic model for local cancer registries | N/A | Epidemiology | SIO, OBI | OWL |
[40] | Development of ontologies for the public health domain | N/A | Public Health | N/A | OWL |
[61] | Understand cellular responses to different perturbations | LINCS-CLOview | Cell Lines | CLO | OWL |
[53] | Integrate heterogeneous datasets | OCRV | Cancer Outcomes | BFO, NCIt, TEO | OWL |
[47] | Define a specific terminological system to standardized data collection for head and neck cancer patients | ENT COBRA ontology | Clinical | N/A | N/A |
[10] | Use structured knowledge representation with concepts of treatment end points | CCTOO | Clinical | NCIt, CTCAE | OBO |
[62] | Represent the data elements identified by the synoptic worksheets of College of American Pathologists | SNOMED CT observable ontology | Clinical | SNOMED CT, LOINC | N/A |
[35] | Create a standardized hierarchic ontology of cancer treatments, mapped to standard nomenclatures | N/A | Cancer Treatments | HemOnc | OWL |
[56] | Increase interoperability between data sources to allow the creation of Big Data studies involving several treatment centers | ROS | Radiation Oncology | FMA | OWL |
[36] | Create temporal ontology of survival outcome measures of clinical trials in oncology | TOCSOC | Clinical | EFO, CCTOO, IOBC, NCIT | OWL |
[60] | Provide an ontological representation of immunophenotyping cell types found in hematologic malignancies | CCL | Hematologic Malignancies | CL | OWL |
[42] | Semi-automatic development of CHV for breast cancer | MuEVo | Clinical | MeSH, MedDRA, SNOMEDint | SKOS |
[44] | Offer ontology-based approach modeling HCC tumors | OntHCC | Liver Cancer | N/A | OWL |
[57] | Support integrative data analysis in cancer outcomes research | ODVDS | Risk Factors | BFO | OWL |
[58] | Cytological tissue image analysis of cervical cancer | CCOWL | Cervical Cancer | N/A | OWL |
[31] | Standardize the terminology used in the selection and integration steps of RF variables and data sources | OD-ATTEST | Risk Factors | BFO, others in NCBO (not specified) | OWL |
[48] | Standardize data collection for non-melanoma skin cancer patients treated with brachytherapy | SKIN-COBRA ontology | Clinical | N/A | N/A |
[43] | Analyze social media data to identify information needs and emotions related to cancer | N/A | Social | LCO, BCO, GCO, SOSW | N/A |
[37] | Solve the heterogeneity and diversity of different data types related to prostate cancer by establishing a standardized lifestyle ontology | PCLiON | Risk Factors | NCIT, WordNet, SNOMED CT, The Cochrane Library, FooDB, CheBI | OWL |
[59] | Build a knowledge graph that represents causal associations between incidence of breast cancer and risk factors | RiskExplorer | Clinical | UMLS | N/A |
[30] | Facilitate the integrity and maintenance of ENCR core data set. | ENCR core-data | Epidemiology | N/A | OWL |
[14] | Minimizing vagueness in the formalization of medical knowledge | BCFO | Clinical | DO | OWL |
[52] | Predict side effects of bladder cancer treatments | N/A | Bladder Cancer | N/A | OWL |
[38] | Provide a generalizing pattern of more concise definitions to correctly classify all tumor configurations | N/A | Gastrointestinal Tumors | BioTopLite2 | N/A |
5. Ontologies and Knowledge Graph Applications in Cancer Research
5.1. Terminology-Focused Applications
5.1.1. Data Annotation
5.1.2. Data Integration
5.1.3. Database Interfaces
5.1.4. Natural Language Processing
Ref | Summary | Ontologies | Data | Tag | Cancer Type |
---|---|---|---|---|---|
[51] | Ontology for a clinical decision support system to produce treatment recommendations | SNOMED-CT, New ontology | N/A | Database Interface | Lung |
[74] | Ontology-based querying for cancer research data | NCIt | N/A | Database Interface | Various |
[77] | Mining of genetic marker data in a journal | SNOMED-CT, HUGO | NEJM | NLP | Various |
[11] | Automatic translation of NeoMark relational database | BFO, RO, OBI, OGMS, HDO | NeoMark database | Data Integration | OSCC |
[15] | Manual identification and inference of associations between breast cancer drugs | New ontology | PharmGKB, NCI | Data Annotation | Breast |
[65] | Genome-wide functional predictions of lncRNAs | GO | Gencode, Ensembl, ENCODE project LncRNA Ontology | Data Integration | Various |
[66] | Extraction of semantic entities in eligibility criteria and annotation | UMLS | CTG | Data Integration, Database Interface, NLP | Breast |
[34] | Development of an ontology-driven survivor engagement framework for mobile apps | FOAF | N/A | Database Interface, Data Annotation | POCS |
[67] | Prediction of clinical outcomes from a graph-based approach with multi-omics and genetic data | GO | TCGA | Data Integration | Ovarian |
[68] | Development of a focused view within the DO from cancer datasets | DO | COSMIC, TCGA, ICGC, TARGET, IO, EDRN | Data Integration | Various |
[39] | Development of a platform for analysis and visualization of data | ICD10, ICD-O-3, TNM staging, SIO, OBI, OQuaRE | NCRI | Data Annotation, Database Interface | Various |
[13] | Automatic annotation of cancer hallmarks on biomedical literature | MeSH | N/A | Data Annotation, NLP | Various |
[70] | Connection of predictors with cancer survival with a use-case ontology | OCRV | FCDS 2000 U.S. census, BRFSS | Data Integration | Various |
[69] | Data integration of several databases with ontologies to enable querying of patient data | DO, UBERON | TCIA, TCGA, LIDC-IDRI, Head-Neck-PET-CT | Data Integration | Various |
[78] | Construcion of OCRV based on data analysis needs | NCIt, TEO, ICD-O-3, ICD-9-CM | UF Health CCCA, FCDS, ATSDR, USCB, BRFSS, County Health Ranking & Roadmaps | Data Integration | Various |
[64] | Manual representation of semantic temporal components of CDEs | TEO | NCI, caDSR | Data Annotation | Various |
[44] | Ontology built following the MethOntology methodology [79] | DICOM | University Hospital of Clermont-Ferrand | Data Annotation | HCC |
[42] | Semi-automatic development of CHV for breast cancer | INDC dictionary | N/A | NLP | Various |
[71] | KG of cancer registry data, with data analysis and visualization | New ontology | LTR | Data Integration, Database Interface | Various |
[43] | Development of an ontology to understand information needs and emotions | LCO, BCO, GCO, SOSW | N/A | NLP | Various |
[72] | KGHC is a KG constructed from clinical data available publicly | UMLS | PubMed, UpToDate, CTG, SemMedDB | Data Integration | HCC |
[76] | Functional annotation of circRNAs obtained from sequencing lung cell lines | GO | Lung cell lines sequencing data | Database Interface | Lung |
[12] | IMI is a web-based system that creates mappings from the NAACCR data dictionary to NCIt | NAACCR data dictionary, NCIt | KCR | Data Integration, Database Interface | Various |
[73] | Comparative analysis of cancer hallmark mapping strategies | GO | MSigDB, KEGG, cancer hallmark mapping schemes, TCGA | Data Integration | Various |
5.2. Semantic-Focused Applications
5.2.1. Formalized Definitions and Axioms: Reasoning with Ontologies
Ref | Objective | Input Ontologies | Reasoner | Tag | Cancer Type |
---|---|---|---|---|---|
[81] | Determine cancer type and stage of the patient to recommend treatments | LuCO, BCO, LCO | FaCT++ | New Knowledge Inference | Various |
[15] | Identification of new indications for existing drugs | New ontology | Automated semantic inference (Protégé) | New knowledge Inference | Breast |
[82] | Prediction of new drug targets | New ontology | Pellet (Protégé) | New knowledge Inference | Colorectal |
[49] | Extraction of association rules from large datasets on gastric cancer patients | GCO | Apriori algorithm | New Knowledge Inference | Gastric |
[38] | Provide a generalizing pattern of more concise definitions to correctly classify all tumor configurations | New ontology | HermiT DL (Protégé) | Error Detection | Various |
[46] | Creation of TNM-O | FMA, BioTopLite 2 | HermIT DL | Error Detection | Various |
[52] | Predict side effects of bladder cancer treatments | New ontology | Pellet (Protégé) | New knowledge Inference + Error Detection | Bladder |
[83] | Signal rule violations in a validation process of multiple primary tumors international rules | ICD-O-3 | FaCT++, HermiT | New knowledge Inference + Error Detection | Multiple primary tumors |
[30] | Facilitate the integrity and maintenance of ENCR core data set | New ontology | FaCT++ (Protégé) | Error Detection | Various |
[14] | Minimizing vagueness in the formalization of medical knowledge | DO | Fuzzy DL, HermiT/Pellet (Protégé) | Error Detection | Breast |
5.2.2. Mining and Analyzing Multimodal Data with Ontologies
Ref | Objective | Method | Input Ontologies | Input Data | Tag | Cancer Type |
---|---|---|---|---|---|---|
[77] | Mining of genetic marker data in a journal | MCVS NLP engine | SNOMED CT, HUGO | NEJM | ML | Various |
[74] | Ontology-based querying for cancer research data | Construction of a OWL Generation facility | NCIt | caGrid | ML | Various |
[11] | Represent the project domain and link the NeoMark data to other domains | Bayesian Networks, ANN, SVMs, Decision Trees, Random Forests | BFO, RO, OBI, OGMS, HDO | N/A | ML | OSCC |
[50] | Cancer reclassification and drug inference | Vazquez Bayesian clustering algorithm | N/A | HemOnc.org | ML | Various |
[19] | Ontological application in Clinical Decision Support | CBR and MAS | UML | Patient Health Records | ML | Gastric |
[82] | Prediction of new drug targets | KEGG functional PharmGKB drug annotation. Network neighborhood modeling ranking | New ontology, ATC | PharmGKB, GAD, CGC, OMIM, NCI, DrugBank, TTD | ML | Colorectal |
[39] | Design of a semantic model for local cancer registries | Ontology-driven search filters and aggregates properties of interest | ICD10, ICD-O-3, TNM staging, SIO, OBI, OQuaRE | NCRI | Filtering | Various |
[90] | Discover patterns related to the patients’ ability to perform daily living activities | AQ21—multi-task ML and data mining system | UMLS | Surveillance, Epidemiology, and End Results—Medicare HOS | ML | Various |
[13] | Automatic annotation of cancer hallmarks on biomedical literature | United Decision Tree and Random Forest | MeSH | Pubmed abstracts | ML | Various |
[85] | Prediction of microRNA related to glucocorticoid resistance | Manual background literature search. Semantic searches in resulting subset | OMIT, NCRO, MeSH | PubMed | Filtering | Pediatric ALL |
[17] | Cancer-related gene prioritization | Fuzzy similarity | GO | GSEA website, TCGA, SNP4Disease | Similarity | PAC, Breast |
[161] | Predict drug synergy in cancer treatment | Stacked Restricted Boltzmann machine | GO, Ontology Fingerprints | AstraZeneca-Sanger Drug Combination Prediction Challenge, GDSC, KEGG | ML | Various |
[18] | Identification of cancer driver genes with role distinction | Neuro-symbolic deep learning on semantic knowledge representation on genetic information | CMPO, GO, MP | Uniprot, MGI database, Mutational Cancer Drivers Database, CPD | ML | Naso-pharyngeal, Colorectal |
[87] | Identification of relevant, expression data non-redundant cancer gene markers | Unsupervised Multi-View Multi-Objective clustering | GO | Gene expression datasets from own lab | ML | Prostate, DLBCL, FL |
[58] | Predict cervical cancer cells from cytological tissue images | DNN | New ontology | hospital cervical cancer data, kaggle data repository | ML | Cervical |
[88] | Complement system role inference from immunofunctionome analysis | SVMs | GO | GEO database | ML | OCCC |
[89] | Cancer detection based on gene expression data | Multilayer Perceptrons | GO | Affymetrix HG-U133Plus2 chip arrays, TCGA | ML | Various |
[91] | Tolerating data missing in breast cancer diagnosis from clinical ultrasound reports | KG embeddings | BI-RADS | Ultrasound reports | ML | Breast |
[92] | Real-time inference on a lung KG | GAT | New ontology | KEGG, Uniprot, DrugBank, TCGA | ML | Lung |
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ATC | Anatomical Therapeutic Chemical |
ATSDR | Agency for Toxic Substances and Disease Registry |
ALL | Acute Lymphoblastic Leukemia |
ANN | Artificial Neural Network |
BCFO | Breast Cancer Fuzzy Ontology |
BCO | Breast Cancer Ontology |
BFO | Basic Formal Ontology |
BRFSS | Behavioral Risk Factor Surveillance System |
BTL2 | BioTopLite 2 |
caDSR | Cancer Data Standards Repository |
CBR | Case-Based Reasoning |
CCL | Cancer Cell Ontology |
CCTOO | Cancer Care Treatment Outcome Ontology |
CDEs | Common Data Elements |
CGC | Cancer Gene Census |
CHV | Consumer Health Vocabulary |
CL | Cell Ontology |
CLO | Cell Line Ontology |
CMPO | Cellular Microscopy Phenotype Ontology |
COBRA | COnsortium for BRachytherapy data Analysis |
COnQueSt | Cancer Ontology Querying System |
CPD | Cellular Phenotype Database |
CTCAE | Common Terminology Criteria for Adverse Events |
CTG | ClinicalTrials.gov |
DICOM | Digital Imaging and Communications in Medicine |
DL | Description Logic |
DLBCL | Diffuse Large B Cell Lymphoma |
DO | Disease Ontology |
EFO | Experimental Factor Ontology |
ENCR | European Network of Cancer Registries |
ENCR core-data | European Cancer-Registry core-data ontology |
FCDS | Florida Cancer Data System |
FL | Follicular Lymphoma |
FMA | Foundational Model of Anatomy |
FOAF | Friend of a Friend ontology |
FOORC | Fuzzy Ontology for Obesity-Related Cancer |
GAD | Genetic Association Database |
GCO | Gastric Cancer Ontology |
GDSC | Genomics of Drug Sensitivity in Cancer |
GO | Gene Ontology |
HDO | Human Disease Ontology |
HCC | Hepatocellular Carcinoma |
HOS | Health Outcomes Survey |
HUGO Gene Nomenclature | Human Genome Organization Gene Nomenclature |
ICD-9-CM | International Classification of Diseases Ninth Revision Clinical |
Modification | |
ICD-O-3 | International Classification of Disease for Oncology 3rd edition |
IMI | Interactive Mapping Interface |
IOBC | Interlinking Ontology for Biological Concepts |
KCR | Kentucky Cancer Registry |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
KG | Knowledge Graph |
LCO | Liver Cancer Ontology |
LCKGO | Lung Cancer Knowledge Graph Ontology |
LINCS | Library of Integrated Network-based Cellular Signatures |
lncRNAs | long non-coding RNAs |
LOINC | Logical Observation Identifier Names and Codes |
LTR | Louisiana Tumor Registry |
LuCO | Lung Cancer Ontology |
MAS | Multi-Agent System |
MCVS | Multi-threaded Clinical Vocabulary Server |
MedDRA | Medical Dictionary for Regulatory Activities |
MeSH | Medical Subject Headings |
MGI | Mouse Genome Informatics |
ML | Machine Learning |
MP | Mammalian Phenotype ontology |
MuEVo | Multi-Expertise Vocabulary |
NAACCR | North American Association of Central Cancer Registries |
NCI | National Cancer Institute |
NCIt | National Cancer Institute Thesaurus |
NCRI | National Cancer Registry Ireland |
NCRO | Non-Coding RNA Ontology |
NEJM | New England Journal of Medicine |
NLP | Natural Language Processing |
OBDA | Ontology-Based Data Access |
OBI | Ontology for Biomedical Investigators |
OCCC | Ovarian clear cell carcinoma |
OCRV | Ontology for Cancer Research Variables |
OCRSEV | Ontology of Cancer Related Social-Ecological Variables |
OD-ATTEST | Ontology for the Documentation of vAriable selecTion and daTa |
sourcE Selection and inTegration | |
ODVDS | Ontology for Documentation of Variable and Data Source |
OGMS | Ontology of General Medical Science |
OIE | Open Information Extraction |
OMIM | Online Mendelian Inheritance in Man |
OMIT | Ontology for MicroRNA Target |
OntHCC | Ontology of Hepatocellular Carcinoma |
OQuaRE | Ontology Quality Evaluation Framework |
OSCC | Oral Squamous Cell Carcinoma |
OWL | Web Ontology Language |
PAC | Prostatic Adenocarcinoma |
POCS | Profile Ontology for Cancer Survivors |
QHIO | Quantitative Histopathological Imaging Ontology |
RO | Relation Ontology |
ROS | Radiation Oncology Structures |
SCRS | Semantic Cancer Registry System |
SEER-MHOS | Surveillance, Epidemiology, and End Results—Medicare Health |
Outcomes Survey | |
SIO | Semanticscience Integrated Ontology |
SKOS | Simple Knowledge Organization System |
SNOMED CT | Systematized Nomenclature of Medicine Clinical Terms |
SNOMEDint | SNOMED International |
SOSW | Sentiment Ontology for Social Web |
SVMs | Support Vector Machines |
SWIT | Semantic Web Integration Tool |
TCGA | The Cancer Genome Atlas |
TEO | Time Event Ontology |
TNM | Tumor–Node–Metastasis |
TNM-O | Tumor–Node–Metastasis Ontology |
TOCSOC | Temporal Ontology for Comparing the Survival Outcomes |
TTD | Therapeutic Target Database |
UMLS | Unified Medical Language System |
USCB | United States Census Bureau |
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Silva, M.C.; Eugénio, P.; Faria, D.; Pesquita, C. Ontologies and Knowledge Graphs in Oncology Research. Cancers 2022, 14, 1906. https://doi.org/10.3390/cancers14081906
Silva MC, Eugénio P, Faria D, Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers. 2022; 14(8):1906. https://doi.org/10.3390/cancers14081906
Chicago/Turabian StyleSilva, Marta Contreiras, Patrícia Eugénio, Daniel Faria, and Catia Pesquita. 2022. "Ontologies and Knowledge Graphs in Oncology Research" Cancers 14, no. 8: 1906. https://doi.org/10.3390/cancers14081906