=Paper=
{{Paper
|id=Vol-2306/paper5
|storemode=property
|title=Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics
|pdfUrl=https://ceur-ws.org/Vol-2306/paper5.pdf
|volume=Vol-2306
|authors=Pierre Monnin
|dblpUrl=https://dblp.org/rec/conf/ekaw/Monnin18
}}
==Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics==
https://ceur-ws.org/Vol-2306/paper5.pdf
Discovering and Comparing Relational
Knowledge, the Example of Pharmacogenomics
Pierre Monnin
Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
pierre.monnin@loria.fr
Abstract. Pharmacogenomics (PGx) studies the influence of the genome
in drug response, with knowledge units of the form of ternary relation-
ships genomic variation – drug – phenotype. State-of-the-art PGx knowl-
edge is available in the biomedical literature as well as in specialized
knowledge bases. Additionally, Electronic Health Records of hospitals
can be mined to discover such knowledge units that can then be com-
pared with the state of the art, in order to confirm or temper relationships
lacking validation or clinical counterpart. However, both discovering and
comparing PGx relationships face multiple challenges: heterogeneous de-
scriptions of knowledge units (languages, vocabularies and granularities),
missing values and importance of the time dimension. In this research,
we aim at proposing a framework based on Semantic Web technologies
and Formal Concept Analysis to discover, represent and compare PGx
knowledge units. We present the first results, consisting of creating an
integrated knowledge base of PGx knowledge units from various sources
and defining comparison methods, as well as the remaining issues to
tackle.
Keywords: Knowledge Discovery · Knowledge Comparison · Seman-
tic Web · Ontology · Formal Concept Analysis · Pharmacogenomics
1 Problem Statement
Pharmacogenomics (PGx) is the study of the influence of genomic variations in
the variations in drug response phenotypes. Knowledge in PGx is composed of
ternary relationships of the form genomic variation – drug – phenotype, where
the phenotype can be the expected drug outcome or an adverse effect. For exam-
ple, one well studied PGx relationship is G6PD:202A – chloroquine – anemia,
stating that patients having the 202A version of the G6PD gene and treated with
chloroquine will experience anemia. PGx is of importance in the implementation
of personalized medicine, where drug treatments and drug doses are tailored to
the genotype of patients to reduce risks of adverse effects.
State-of-the-art PGx knowledge can be found in (i) specialized knowledge
bases (e.g., PharmGKB) and (ii) the biomedical literature. However, such rela-
tionships may have only been observed on reduced cohorts of patients and still re-
main to be further studied. On the other hand, nowadays, lots of health care data
are digitally available thanks to the use of Electronic Health Records (EHRs).
They contain information about diseases, laboratory tests, medical procedures
and prescriptions that a patient has experienced. Therefore, mining EHRs to
discover PGx knowledge units and then comparing them with the state of the
art could confirm or temper poorly validated relationships [7].
However, mining PGx relationships from EHRs and comparing knowledge
units from various sources face multiple challenges. First, EHRs data are mul-
tivariate, heterogeneous, irregular in time, and sparse [2]. The time dimension
should also be carefully taken into account and relationships from a patient level
should be generalized to an aggregated level. Then, comparing PGx knowledge
units from various sources require to face the heterogeneity of their descriptions
in terms of languages, vocabularies and granularities. For example, as genetic
data are not common in EHRs, mined relationships will most likely use proxies,
such as lab measures or specific side effects to a drug treatment that indicate
the presence of a specific genomic variation. Knowledge comparison mechanisms
should also leverage provenance metadata of knowledge units. Indeed, sources
may define quality metrics associated with PGx relationships and representing
their “level of validation”. Finally, one challenge also resides in dealing with con-
tradictory information coming from different sources. Therefore, in this research,
we aim at proposing representation formalisms and discovery and comparison
methods in the context of relational and heterogeneous knowledge, formed by
PGx relationships.
This paper is organized as follows. Section 2 presents some relevant works
w.r.t. the considered challenges. Sections 3 and 4 present the proposed approach
as well as the adopted methodology. Section 5 introduces the first results that
are discussed in Section 6.
2 State of the Art
To propose a framework for knowledge discovery and comparison in pharma-
cogenomics, one first needs to define the knowledge representation formalism
to adopt. In this direction, one possible solution resides in using Semantic Web
technologies, such as OWL and RDF, that allow to represent data and knowl-
edge in a machine-readable format. Such data and knowledge being published
and accessible on the Web, it is possible to leverage existing knowledge de-
fined elsewhere when considering a particular data set. Existing works already
use Semantic Web technologies to represent Life Sciences data and knowledge.
For example, the Bio2RDF project [5] represents and interlinks numerous Life
Sciences data sets about drugs, genomic variations, phenotypes, etc. More par-
ticularly, ontologies have been created for the PGx domain. However, they are
not adapted to the current need of integrating and comparing knowledge units
of various sources. For example, the Pharmacogenomic Clinical Decision Sup-
port (or Genomics CDS) [19] aims at applying pharmacogenomic guidelines to
patient data, to help clinicians in their decisions. Alternatively, the Suggested
Ontology for Pharmacogenomics (SO-Phare) [8] focuses on knowledge discovery.
Besides representing PGx knowledge itself, Semantic Web technologies are
also used to represent EHRs data. For example, Odgers and Dumontier [18]
showed that transforming EHRs data into RDF allowed to connect them with
additional knowledge, in the objective of improving knowledge discovery meth-
ods. Similarly, Beyan and Decker [4] proposed to use RDF to model temporal
relations in EHRs. Indeed, the flexible schema of RDF graphs seems suited to the
sparsity and heterogeneousness of EHRs data. Moreover, RDF allows to connect
data to existing knowledge repositories. Semantic relations as well as hierarchies
of concepts provided by ontologies can also be used to abstract care trajectories
of patients.
Mining such abstract care trajectories may be considered as mining sequences
of events. Several approaches have already been proposed. One possible approach
is to consider Allen’s temporal logics to mine temporal patterns, abstracting from
exact durations between events [3]. Additionally, lots of health care data are de-
scribed using concepts of ontologies. For example, drug prescriptions can be
encoded using the Anatomical Therapeutic Chemical Classification (ATC). It is
also frequent that diseases are encoded with concepts of the International Clas-
sification of Diseases (ICD). These ontologies provide hierarchies of concepts,
that can be used as background knowledge when abstracting care trajectories.
For example, Egho et al. [9] proposed to mine heterogeneous multidimensional se-
quential patterns, taking into account background knowledge represented within
ontologies.
Finally, knowledge discovery from EHRs represented as RDF graphs or com-
paring knowledge in RDF graphs can be achieved using Formal Concept Analysis
(FCA) [11]. Indeed, FCA is a mathematical framework grouping objects w.r.t.
their common attributes in formal concepts. These formal concepts are orga-
nized in a hierarchical structure called a lattice, where the hierarchy indicates
a specialization of sets of grouped objects (or dually a generalization of sets of
associated attributes). FCA has been extended with Pattern Structures [10] to
take into account more complex data than just objects and attributes. FCA and
Pattern Structures were already applied on ontology engineering tasks such as
mining definitions of ontology concepts [1] and analyzing ontology-based anno-
tations in biomedical documents [6].
3 Proposed Approach
In this research, we propose to develop a framework based on Semantic Web
technologies and Formal Concept Analysis to discover, represent, and compare
(or align) PGx knowledge units from various sources, as presented in Figure 1.
It should be noted that the RDF triples considered in this work can be seen as
data but also as knowledge.
A first major task resides in building an integrated knowledge base of PGx
knowledge units from various sources. To this aim, a common integration schema
should be defined. As previously mentioned, existing PGx ontologies are not
suited to the current need as they focus on knowledge discovery or reasoning
Existing knowledge bases
RDFization
EHRs EHRs Knowledge
Knowledge Base discovery
RDFization Knowledge
Biomedical comparison
literature
Integrated knowledge
base of PGx
RDFization knowledge units
Specialized
databases
Fig. 1. Proposed framework for knowledge discovery, representation, and comparison
in PGx.
instead of integration. This integration schema needs to provide an encoding for
provenance metadata. Indeed, the knowledge comparison mechanisms could use
information such as quality metrics defined in original sources or by knowledge
discovery algorithms (e.g., confidence, support). Finally, provenance metadata
could also encode parameters or versions of the developed knowledge discovery
algorithms, allowing to compare and evaluate different executions. The flexibility
of Semantic Web technologies in defining an integration schema is particularly
adapted to our case as PGx knowledge units can be partially discovered from
the various considered sources.
By representing PGx knowledge units with Semantic Web technologies, they
can be interlinked with knowledge defined elsewhere. This background knowl-
edge can be of particular interest when comparing knowledge units. Indeed, on-
tologies provide equivalences or subsumption relationships, that could improve
identifying equivalent or more specific PGx relationships. For this comparison
mechanism, we choose to use Formal Concept Analysis. Indeed, FCA groups
similar objects together in formal concepts, which can be used to identify simi-
lar PGx relationships. Additionally, the hierarchical structure of the generated
lattice can be leveraged to identify relationships more specific or more general
than others. Finally, ontologies describing components of PGx relationships and
their provided concepts hierarchies can be taken into account by using Pattern
Structures.
Finally, we choose to use a two-step approach for knowledge discovery from
EHRs. First, EHRs data should be transformed into RDF. The resulting knowl-
edge base can then be mined. As previously mentioned, this RDFization allows
to use existing knowledge in the mining algorithms. To mine the RDF represen-
tation of EHRs data, FCA can also be considered as patients undergoing similar
care trajectories will be grouped into the same formal concepts. Pattern Struc-
tures can be used to express sequences representing these care trajectories while
benefiting from knowledge defined in ontologies.
4 Methodology
This research work is organized around two main tasks: knowledge discovery
from EHRs and knowledge comparison. These two tasks present different method-
ologies and validation mechanisms.
The methodology for the task of comparing knowledge units can be sketched
out as follows:
(1) Define a common integration schema for representing PGx knowledge units
from various sources and an encoding for their provenance metadata;
(2) Instantiate this schema with knowledge units from various sources, validating
the suitability of the schema to represent these knowledge units and their
provenance metadata;
(3) Define and execute comparison methods on the knowledge base resulting
from the instantiation process.
It is noteworthy that validating comparison methods will require an expert, to
be able to identify whether suggestions of identical, more specific or related
relationships are correct. However, we can also develop a first naive comparison
method based on domain knowledge rules, constituting a first baseline to be
compared with results of advanced methods.
Regarding the task of knowledge discovery from EHRs, as we chose to use a
representation using Semantic Web technologies, the methodology should be as
follows:
(1) Transform EHRs data into a knowledge base, connect it with existing knowl-
edge defined elsewhere;
(2) Mine the resulting knowledge base for PGx knowledge units;
(3) Validate the discovered knowledge units.
Similarly to the task of comparing knowledge units, the discovery of PGx knowl-
edge units should be validated by a domain expert. However, a first validation
step of the discovery algorithms could consist in re-discovering PGx relationships
already stated in the biomedical literature from a specific cohort of patients.
5 Results
Learning from the existing ontologies for PGx, we built PGxO, a simple ontol-
ogy only representing the aspects of PGx needed for integrating and comparing
knowledge units from various sources [13]. Based on the W3C Recommendation
PROV-O, our work also provides a flexible encoding for provenance metadata,
e.g., the source of the knowledge units, quality metrics, etc. The ontology and
the encoding for provenance metadata were validated by answering competency
questions. In a first evaluation, we manually instantiated PGxO with knowledge
units from (i) PharmGKB, (ii) the literature and (iii) what we though may be
discovered in EHRs. In a later evaluation, we instantiated the ontology auto-
matically with knowledge units extracted programmatically from PharmGKB
and the biomedical literature and manually by representing results reported in
studies of EHRs [14]. The resulting integrated data set is called PGxLOD.
As a first comparison work, we defined a set of simple reconciliation rules [14],
identifying when two PGx relationships are referring to the same knowledge
unit, if one is a more precise version of the other or if they are related (to some
extents). These conclusions are then added to the integrated knowledge base.
For example, one rule states that if two PGx relationships involve the same
sets of drugs, genomic variations and phenotypes, then they represent the same
knowledge unit. Results of executing these reconciliation rules constitute a first
baseline to be compared with advanced reconciliation methods.
Finally, regarding comparison methods, we started investigating how FCA
could be used to compare knowledge units. In order to compare the class hierar-
chy of an ontology with the hierarchy formed by a lattice grouping individuals
w.r.t. the predicates they are subject of, we defined the notion of concept an-
notation [15, 16]. Each formal concept is annotated with the ontology classes
instantiated by all the individuals of the concept. Subsumption axioms are then
read from the annotated structure and compared with those already defined in
the considered ontology. Using multiple annotations with multiple ontologies, it
is also possible to suggest equivalence relationships between classes of different
ontologies [17]. Finally, by grouping individuals w.r.t. other individuals they are
associated with, it is possible to generalize relationships between individuals to
relationships between classes of individuals. This can be seen as a way to de-
scribe frequent profiles of predicates, for example families of genes frequently
associated with families of drugs.
6 Discussion
Our first results in integrating knowledge units from various sources validate our
ontology and the encoding of provenance metadata. The reconciliation rules con-
stitute an interesting baseline that will be useful when executing more complex
comparison methods. Indeed, these advanced methods should yield the same (or
more) comparison results than the reconciliation rules. Additionally, executing
the reconciliation rules on PGxLOD led to identify a major shortcoming. As
PGx relationships are compared based on the involved drugs, genomic varia-
tions and phenotypes, mapping relations identifying equivalent or more precise
components are of importance. Therefore, it is important to improve and com-
plete existing mappings, possibly leveraging automatic mappings generated by
ontology repositories such as the NCBO Bioportal.
When such mappings are missing, we could leverage unqualified relations be-
tween individuals, such as x-ref relations. Indeed, they could be used to define
or learn similarities between individuals, instead of equivalences. Therefore, one
future challenge would be to define and use comparison methods taking into
account such similarities, leading to identify similar but not strictly equivalent
PGx relationships. Additionally, as PGx knowledge units are ternary relation-
ships, one next challenge resides in using Triadic Analysis [12], an extension
of FCA for ternary relations, with Pattern Structures to integrate background
knowledge from ontologies and similarities.
Finally, results of knowledge comparison approaches can be seen as iden-
tifying identical knowledge units, more precise ones, and related ones (to some
extents). However, we could also envision the occurrence of contradictory knowl-
edge units. In this case, some questions of interest reside in the definition of a
contradiction as well as its discovery and its representation.
Acknowledgments
I would like to thank my supervisors Adrien Coulet and Amedeo Napoli and my
co-authors Clément Jonquet, Joël Legrand, Mario Lezoche and Andon Tchechmed-
jiev for our on-going work. This work is supported by the PractiKPharma
project, founded by the French National Research Agency (ANR) under Grant
No. ANR-15-CE23-0028, and by the Snowball Inria Associate Team.
References
1. Alam, M., Buzmakov, A., Codocedo, V., Napoli, A.: Mining definitions from RDF
annotations using formal concept analysis. In: Proceedings of the Twenty-Fourth
International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires,
Argentina, July 25-31, 2015. pp. 823–829 (2015), http://ijcai.org/Abstract/15/121
2. Batal, I.: Temporal data mining for healthcare data. In: Healthcare Data Analyt-
ics., pp. 379–402 (2015), http://www.crcnetbase.com/doi/abs/10.1201/b18588-14
3. Batal, I., Fradkin, D., Jr., J.H.H., Moerchen, F., Hauskrecht, M.: Mining re-
cent temporal patterns for event detection in multivariate time series data. In:
The 18th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining, KDD ’12, Beijing, China, August 12-16, 2012. pp. 280–288 (2012).
https://doi.org/10.1145/2339530.2339578
4. Beyan, O.D., Decker, S.: An RDF based semantic approach to model temporal
relations in health records. In: Proceedings of the 9th International Conference
Semantic Web Applications and Tools for Life Sciences, Amsterdam, The Nether-
lands, December 5-8, 2016. (2016), http://ceur-ws.org/Vol-1795/paper6.pdf
5. Callahan, A., Cruz-Toledo, J., Ansell, P., Dumontier, M.: Bio2rdf release 2: Im-
proved coverage, interoperability and provenance of life science linked data. In: The
Semantic Web: Semantics and Big Data, 10th International Conference, ESWC
2013, Montpellier, France, May 26-30, 2013. Proceedings. pp. 200–212 (2013).
https://doi.org/10.1007/978-3-642-38288-8_14
6. Coulet, A., Domenach, F., Kaytoue, M., Napoli, A.: Using pattern structures
for analyzing ontology-based annotations of biomedical data. In: Formal Con-
cept Analysis, 11th International Conference, ICFCA 2013, Dresden, Germany,
May 21-24, 2013. Proceedings. pp. 76–91 (2013). https://doi.org/10.1007/978-3-
642-38317-5_5
7. Coulet, A., Smaïl-Tabbone, M.: Mining electronic health records to val-
idate knowledge in pharmacogenomics. ERCIM News 2016(104) (2016),
http://ercim-news.ercim.eu/en104/special/mining-electronic-health-records-to-
validate-knowledge-in-pharmacogenomics
8. Coulet, A., Smaïl-Tabbone, M., Napoli, A., Devignes, M.: Suggested ontology for
pharmacogenomics (so-pharm): Modular construction and preliminary testing. In:
On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops, OTM
Confederated International Workshops and Posters, AWeSOMe, CAMS, COM-
INF, IS, KSinBIT, MIOS-CIAO, MONET, OnToContent, ORM, PerSys, OTM
Academy Doctoral Consortium, RDDS, SWWS, and SeBGIS 2006, Montpellier,
France, October 29 - November 3, 2006. Proceedings, Part I. pp. 648–657 (2006).
https://doi.org/10.1007/11915034_89
9. Egho, E., Raïssi, C., Jay, N., Napoli, A.: Mining heterogeneous multidimen-
sional sequential patterns. In: ECAI 2014 - 21st European Conference on Arti-
ficial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Pres-
tigious Applications of Intelligent Systems (PAIS 2014). pp. 279–284 (2014).
https://doi.org/10.3233/978-1-61499-419-0-279
10. Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Concep-
tual Structures: Broadening the Base, 9th International Conference on Conceptual
Structures, ICCS 2001, Stanford, CA, USA, July 30-August 3, 2001, Proceedings.
pp. 129–142 (2001). https://doi.org/10.1007/3-540-44583-8_10
11. Ganter, B., Wille, R.: Formal concept analysis: mathematical foundations. Springer
Science & Business Media (2012)
12. Lehmann, F., Wille, R.: A triadic approach to formal concept analysis. In: Con-
ceptual Structures: Applications, Implementation and Theory, Third International
Conference on Conceptual Structures, ICCS ’95, Santa Cruz, California, USA, Au-
gust 14-18, 1995, Proceedings. pp. 32–43 (1995). https://doi.org/10.1007/3-540-
60161-9_27
13. Monnin, P., Jonquet, C., Legrand, J., Napoli, A., Coulet, A.: PGxO: A very lite
ontology to reconcile pharmacogenomic knowledge units. PeerJ PrePrints 5, e3140
(2017). https://doi.org/10.7287/peerj.preprints.3140v1
14. Monnin, P., Legrand, J., Husson, G., Ringot, P., Tchechmedjiev, A., Jonquet, C.,
Napoli, A., Coulet, A.: PGxO and PGxLOD: a reconciliation of pharmacogenomic
knowledge of various provenances, enabling further comparison. bioRxiv p. 390971
(2018). https://doi.org/10.1101/390971
15. Monnin, P., Lezoche, M., Napoli, A., Coulet, A.: Using Formal Concept Anal-
ysis for checking the structure of an ontology in LOD: the example of dbpe-
dia. In: Foundations of Intelligent Systems - 23rd International Symposium, IS-
MIS 2017, Warsaw, Poland, June 26-29, 2017, Proceedings. pp. 674–683 (2017).
https://doi.org/10.1007/978-3-319-60438-1_66
16. Monnin, P., Napoli, A., Coulet, A.: Discovering subsumption axioms with con-
cept annotation. In: Gestion de Données—Principes, Technologies et Applications
(BDA 2017) (2017)
17. Monnin, P., Napoli, A., Coulet, A.: Combining Concept Annotation and Pattern
Structures for guiding ontology mapping. In: Proceedings of the 6th International
Workshop ”What can FCA do for Artificial Intelligence”? co-located with Inter-
national Joint Conference on Artificial Intelligence and European Conference on
Artificial Intelligence (IJCAI/ECAI 2018), Stockholm, Sweden, July 13, 2018. pp.
117–126 (2018), http://ceur-ws.org/Vol-2149/paper11.pdf
18. Odgers, D.J., Dumontier, M.: Mining electronic health records using linked data.
AMIA Summits on Translational Science Proceedings 2015, 217 (2015)
19. Samwald, M., Miñarro-Giménez, J.A., Boyce, R.D., Freimuth, R.R., Adlassnig,
K., Dumontier, M.: Pharmacogenomic knowledge representation, reasoning and
genome-based clinical decision support based on OWL 2 DL ontologies. BMC Med.
Inf. & Decision Making 15, 12 (2015). https://doi.org/10.1186/s12911-015-0130-1