=Paper=
{{Paper
|id=Vol-3415/paper-4
|storemode=property
|title=Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived
from Biomedical Literature in the PubChemRDF
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-4.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Li0ZCYB23
}}
==Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived
from Biomedical Literature in the PubChemRDF==
https://ceur-ws.org/Vol-3415/paper-4.pdf
Resource Description Framework (RDF) Modeling of Named
Entity Co-occurrences Derived from Biomedical Literature in the
PubChemRDF
Qingliang Li 1, Sunghwan Kim 1, Leonid Zaslavsky 1, Tiejun Cheng 1, Bo Yu 1 and Evan E.
Bolton 1
1
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health,
Bethesda, MD 20894, USA
Abstract
Named entities, such as chemicals/drugs, diseases, and genes/proteins, and their associations
are not only important components of biomedical literature, but also the foundation of creating
biomedical knowledge bases/graphs. This work details the expression of co-occurrence
associations among chemicals, genes, proteins, and diseases in the Resource Description
Framework (RDF) format within the PubChemRDF resource, which is freely accessible and
publicly available. The co-occurrence model is populated into a triplestore with named entities
and their associations that are derived from text mining of about 35 million biomedical ref-
erences in PubMed. Use cases are provided to demonstrate the utility of the model. Together
with meta-data modeling of the references including the information about the author, journal,
grant, and funding agency, this data model can address pertinent biomedical questions through
SPARQL queries and help exploit bio-medical knowledge in various user perspectives and use
cases.
Keywords 1
PubChem, RDF, Semantic Web, Linked Data, Triplestore, SPARQL, Modeling, Drug,
Chemical, Disease, Gene, Protein, PubChemRDF, Co-Occurrence.
1. Introduction
PubChem (https://pubchem.ncbi.nlm.nih.gov) [1–3] is an open chemical database at the National
Center for Biotechnology Information (NCBI), the National Library of Medicine (NLM), the U.S.
National Institutes of Health (NIH). Among many tools and services provided by PubChem are the
literature knowledge panels [4], which assist users in quickly finding important relationships between
chemicals, genes, proteins, and diseases. The literature knowledge panels for a given entity (e.g., a
chemical, gene, or protein) display a selected set of “co-occurrence neighbors”, which are defined as
any chemicals, genes, proteins, and diseases mentioned together with that entity in the biomedical
literature. In addition, the literature knowledge panel provides a sample of PubMed records that co-
mention the entity and those selected co-occurrence neighbors. The list of the co-occurrence neighbors
and relevant PubMed records can be downloaded for further analysis through the download button in
the panel. Note that the use of the term “co-occurrence neighbors” avoids confusion with the existing
2-dimensional (2-D) and 3-dimensional (3-D) chemical structure-based neighbor relationships [5–7].
The 14th International Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS) Conference, February 13–
16, 2023, Basel, Switzerland
EMAIL: qingliang.li@nih.gov (Q. Li); kimsungh@ncbi.nlm.nih.gov (S. Kim); zaslavsk@ncbi.nlm.nih.gov (L. Zaslavsky);
chengt2@ncbi.nlm.nih.gov (T. Cheng) ; bo.yu2@nih.gov (B. Yu); bolton@ncbi.nlm.nih.gov (E. E. Bolton)
ORCID: 0000-0002-6453-236X (Q. Li); 0000-0001-9828-2074 (S. Kim); 0000-0001-5873-4873 (L. Zaslavsky); 0000-0002-4486-3356 (T.
Cheng); 0000-0003-3952-8921 (B. Yu); 0000-0002-5959-6190 (E. E. Bolton)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The underlying data for the knowledge panel is derived from text mining the 35 million biomedical
references in PubMed, using the LeadMine software [8]. Chemicals, genes, proteins, and diseases are
extracted from the titles and abstracts of the PubMed records and the most relevant co-occurrence
neighbors are identified using statistical analysis and relevance-based sampling. A detailed explanation
of the method used to develop the knowledge panel is given in our previous paper [4].
The present paper describes a data model that expresses the named entities and their co-occurrence
associations in the Resource Description Framework (RDF) format [9], along with the meta-data
modeling of the reference information (including the author, journal, grant, and funding agency). The
data model augments the existing RDF-formatted data (also known as PubChemRDF) [10] and helps
find answers to biomedical questions (as demonstrated in a recent study [11]) through SPARQL
Protocol and RDF Query Language queries [12].
2. Model design
The purpose of the model design is (1) to semantically describe the co-occurrence relations of the
named entities (i.e., chemicals, genes/proteins, and diseases), which are derived from the biomedical
literature, and (2) to take advantage of the existing PubChemRDF graph to address sophisticated
biomedical questions in a semantic way through RDF. In this model, the term “compound” is used to
indicate a chemical named entity to keep consistency in the naming convention within PubChemRDF.
Because gene and protein names are often used interchangeably in literature, the data model designed
in this study does not distinguish genes and proteins and, instead, treats all of them as genes, unless
explicitly specified.
Figure 1 shows the design of the co-occurrence RDF model. There are five types of nodes in this
model:
• Green ovals corresponding to named entities (i.e., compounds, genes, and diseases)
• Blue ovals representing references and their metadata (e.g., author, journal, grant, funding
agency)
• Purple ovals for the co-occurrence of named entities (e.g., compound-compound, compound-
gene, and compound-disease associations)
• White rectangles describing the characteristic of an association (e.g., text mining and type of
associations) used for linking to external ontologies.
• White hexagons representing the co-occurrence score of an association (see below).
The relationships between nodes are defined using the PubChem-specific vocabulary as well as several
external ontologies: the Semanticscience Integrated Ontology (SIO) [13], Funding, Research
Administration and Projects Ontology (FRAPO) [14], Dublin Core Metadata Initiative (DCMI)
metadata terms [15], Publishing Requirements for Industry Standard Metadata (PRISM) vocabularies
[16] and EMBRACE Data And Methods (EDAM) ontology [17].
Named entities identified from text mining of 35 million PubMed records in our previous study [4]
are matched to compounds, genes/proteins, and diseases in PubChemRDF. The associations between
the PubMed records and the named entities found in them are linked with a predicate,
pcvocab:discussesAsDerivedByTextMining.
When two named entities are identified in a PubMed record, a co-occurrence node is created and
linked to the corresponding named entity nodes, with the rdf:subject and rdf:object
predicates. The co-occurrence node is also linked to the nodes specifying the nature of the co-occurrence
(e.g., the types of co-mentioned named entities and whether they are text-mined or not).
Some entities have thousands of co-occurrence neighbors. For example, the term “neoplasms” co-
occurs with over 47,000 compounds, 28,000 genes, and 6,000 diseases. The importance of individual
entity relationships is quantified using the co-occurrence score (SCO), as explained in our previous study
[4]. It can be considered as a variant of the term frequency-inverse document frequency (TF-IDF) score
[18–21]. The co-occurrence score values are used to identify up to 1,000 co-occurrence neighbors of
each neighbor type (i.e., compounds, genes, and diseases) for a given entity, and the corresponding co-
occurrence nodes are created in the model.
Figure 1: Diagram of RDF co-occurrence modeling of compounds, genes, and diseases, derived from
biomedical literature. Blue ovals correspond to biomedical literature (references) and its metadata.
Green ovals represent named entities (i.e., compounds, genes, and diseases). Purple ovals indicate
the co-occurrence associations between named entities (e.g., compound-disease association, disease-
gene association). White rectangles specify the types of individual associations in terms of external
ontologies. White hexagons indicate the co-occurrence scores.
It is important to note that the co-occurrence neighboring relationships between entities (e.g.,
compound-disease) are not symmetrical due to the asymmetry of the co-occurrence scores and the
truncation of the neighbor lists. For example, when a disease is one of the top 1,000 co-occurrence
neighbors of a chemical, there is no guarantee that the chemical is among the top 1,000 co-occurrence
neighbors of the disease. This asymmetry is reflected in the directed graph in the RDF by means of
designating the subject and the object.
Literature data is modeled in reference nodes, which are linked to the nodes representing their
metadata (e.g., journal, author, publication date, grant number, funding agency, and MeSH terms [22]),
using the relevant terms from FRAPO [14], DCMI [15], and PRISM [16] as predicates.
3. Applications
In this section, we present three use cases of the co-occurrence RDF resource. Here, these use cases
assume that the co-occurrence RDF resource and PubChemRDF data are loaded into the triplestore of
Virtuoso [23]. It is possible to load these data into other triplestores or RDF-aware graph databases,
such as Apache Jena.
3.1. Use Case 1: Diseases Co-occurring with a Chemical
The simplest use case of the co-occurrence RDF is to retrieve named entities commonly mentioned
with a query entity in PubMed articles (e.g., diseases or genes/proteins co-occurring with a chemical).
As an example, Figure 2 shows the SPARQL query that retrieves the top 25 diseases mentioned together
with indomethacin (CID 3715) in terms of their co-occurrence scores [4]. The diseases returned from
the query are listed in Table 1, along with their co-occurrence scores and preferred disease names. The
preferred disease name was retrieved from the PubChemRDF disease subdomain (with the term
prefLabel in the Simple Knowledge Organization System (SKOS) [24] as a predicate).
Figure 2: SPARQL query to retrieve the top 25 diseases commonly mentioned with indomethacin (CID
3715) in literature. The SIO terms SIO_000993 and SIO_000300 mean “chemical-disease
association” and “has a value”, respectively.
Table 1
Top 25 diseases most mentioned with indomethacin (CID 3715), along with their co-occurrence scores
(SCO), returned from the SPARQL query in Figure 2.
SCOa Preferred disease name
58416 Inflammation
56017 Ulcer
51446 Edema
42250 Ductus Arteriosus, Patent
36563 Stomach Ulcer
34696 Pain
34306 Premature Birth
28989 Hypertension
22891 Arthritis
21757 Drug-Related Side Effects and Adverse Reactions
21500 Arthritis, Rheumatoid
20986 Infections
20916 Neoplasms
20628 Blood Platelet Disorders
20014 Hypotension
18336 Depressive Disorder
18081 Hemorrhage
17384 Arthritis, Experimental
16928 Headache
16868 Rheumatic Diseases
15627 Hypoxia
14744 Ischemia
14733 Fever
12385 Osteoarthritis
12070 Bartter Syndrome
a
Derived from the values computed from Formula (3) in Reference [4] by
multiplying by 100 and rounding to the nearest integer.
The most commonly occurring disease with indomethacin in literature is “inflammation”, followed
by “ulcer”. It reflects the fact that indomethacin is a non-steroidal anti-inflammatory drug (NSAID)
and that NSAIDs’ common side effects include stomach ulcer. In essence, this query performs the same
data retrieval task used to create the chemical-disease co-occurrence knowledge panel, available on the
Summary page of indomethacin (https://pubchem.ncbi.nlm.nih.gov/compound/3715
#section=Chemical-Disease-Co-Occurrences-in-Literature). It is noteworthy that the co-occurrence
RDF allows the user to get an arbitrary number of co-occurrence neighbors (up to 1000, as explained
in the Model Design section), while the knowledge panel in the Compound Summary page shows
provides a maximum of 25 co-occurrence neighbors with the current settings.
3.2. Use Case 2: References that Co-mentions a Particular Chemical and
Disease Pair
It is noteworthy that the diseases in Table 1 are related to the input chemical (indomethacin) in
different contexts. For example, indomethacin is used to treat inflammatory diseases like arthritis, while
it is also known to cause stomach ulcer (as a side effect). To understand the context of the relationship
between two entities, it is often necessary to get relevant articles that mention them together. The
SPARQL query for this task is shown in Figure 3, with the indomethacin–inflammation pair as an
example.
Figure 3: SPARQL query to get eight most recent references that mention indomethacin (CID3715) and
inflammation (DZID8173) together.
Table 2
Eight recent PubMed records that mention indomethacin and inflammation together, returned from
the SPARQL query in Figure 3.
Date Journal Title
2022-12-31 Pharm. Biol. Gastroprotective effects of water extract of domesticated
Amauroderma rugosum against several gastric ulcer models in
rats
2022-07-20 Macromol. Biosci. A Straightforward Approach Towards Antibacterial and Anti-
Inflammatory Multifunctional Nanofiber Membranes with
Sustained Drug Release Profiles
2022-07-01 Biomed. AMPK/mTOR-driven autophagy & Nrf2/HO-1 cascade
Pharmacother. modulation by amentoflavone ameliorates indomethacin-
induced gastric ulcer
2022-07-01 Environ. Toxicol. Protective effect of lupeol on arthritis induced by type II
collagen via the suppression of P13K/AKT signaling pathway in
Sprague dawley rats
2022-06-25 Int. J. Pharm. Chitosan/sulfobutylether-β-cyclodextrin based nanoparticles
coated with thiolated hyaluronic acid for indomethacin
ophthalmic delivery
2022-06-14 Prostaglandins Post-mortem changes of prostanoid concentrations in tissues
Other Lipid Mediat. of mice: Impact of fast cervical dislocation and dissection delay
2022-06-07 J. Dairy Sci. Induction of leaky gut by repeated intramuscular injections of
indomethacin to preweaning Holstein calves
2022-06-03 Nat. Prod. Res. Ranunculus species suppress nitric oxide production in LPS-
stimulated RAW 264.7 macrophages
In the query for Use Case 2, while the named entities are specified using the PubChem-specific
vocabulary (pcvocab:discussesAsDerivedByTextMining), the external vocabularies from
DMCI [15] and PRISM [16] are used to get the metadata for the reference (i.e., date, title, and journal).
The result of the query is shown in Table 2.
3.3. Use Case 3: Diseases Implicitly Related to a Chemical via Genes
Use Case 3 intends to identify diseases related to a chemical via genes, by first identifying genes
commonly mentioned with the query chemical and then retrieving diseases co-occurring with those
genes. In this Use Case, while some of the resulting diseases may already be mentioned together with
the query chemical in scientific articles, others may not. This implicit relationship can serve as a good
starting point to formulate a new hypothesis to test in future studies. Figure 4 shows the SPARQL
query with maribavir (CID 471161) as an example.
Figure 4: SPARQL query to get the top ten diseases associated with the gene most commonly
mentioned with maribavir (CID471161). The SIO terms, SIO_001257, SIO_000983, and
SIO_000300 mean “chemical-gene association”, “gene-disease association”, and “has a value”,
respectively.
Maribavir is an antiviral drug approved in 2021 by the U.S. Food and Drug Administration (FDA)
for the treatment of posttransplant cytomegalovirus (CMV) infection. Because of its short history, this
drug has not been mentioned in many papers, compared to old drugs introduced in the market decades
ago (e.g., indomethacin). Table 3 shows the diseases retrieved from the query. The gene most
mentioned together with maribavir is the protein kinase, X-linked (PRKX) gene. While some of the
diseases co-occurring with PRKX are directly co-mentioned with maribavir in literature, other diseases,
including “genetic translocation”, “depressive disorder”, “ischemia”, and “neurodegenerative
diseases”, have not appeared with maribavir in PubMed records, implying implicit associations between
Maribavir and these diseases via the PRKX gene.
Table 3
Top ten diseases associated with the protein kinase, X-linked (PRKX) gene, which is the most co-
occurring gene with maribavir (CID 471161). The diseases were returned from the SPARQL query in
Figure 4.
Preferred disease name Co-occurring with maribavir
Neoplasms Yes
Translocation, Genetic No
Carcinogenesis No
Polyploidy Yes
Infections Yes
Inflammation Yes
Depressive Disorder No
Drug-Related Side Effects and Adverse Reactions Yes
Ischemia No
Neurodegenerative Diseases No
4. Conclusions
In this paper, we described the RDF data model that expresses the co-occurrence associations
between chemicals, genes, and diseases derived from biomedical literature. This data model allows
users to quickly identify chemicals, genes/proteins, and diseases mentioned together with a given named
entity (Use Case 1). In addition, the model can be used to get references that mention two entities
together, helping one to understand the context of the co-occurrence association between the entities
(Use Case 2). It can also be used to find an implicit link between entities that are not mentioned
together, through the common entities associated with them (Use Case 3).
The underlying data used in the co-occurrence RDF was derived from text mining of 35 million
references available in PubMed as shown in our previous study [4]. While this data is also used to
generate the PubChem literature knowledge panel, the co-occurrence RDF enables additional tasks. For
example, with the co-occurrence RDF, the user can work with a large set of relevant co-occurrence
neighbors (up to 1,000) for a given entity and automate this data retrieval task using a computer program
or script. The co-occurrence RDF model is an enhancement to the PubChemRDF ecosystem that can
facilitate exploring biomedical knowledge and seeking new discoveries in a semantic way. More
importantly, it is naturally connecting to other linked data resources in various scientific communities
to greatly enhance the usability and accessibility of biomedical data. The co-occurrence RDF data
generated in this study is freely available at Zenodo [25].
5. Acknowledgements
This work was supported by the National Center for Biotechnology Information of the National
Library of Medicine (NLM), National Institutes of Health.
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