=Paper=
{{Paper
|id=Vol-2042/paper9
|storemode=property
|title=On Improving the Phenotype Acquisition Process Using Semantic Web Technology
|pdfUrl=https://ceur-ws.org/Vol-2042/paper9.pdf
|volume=Vol-2042
|authors=Ernesto Jimenez-Ruiz,Dag Hovland,Laura Slaughter,Tony Handstad,Arild Waaler
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Jimenez-RuizHSH17
}}
==On Improving the Phenotype Acquisition Process Using Semantic Web Technology==
https://ceur-ws.org/Vol-2042/paper9.pdf
On adopting Ontology Alignment techniques
within the Phenotype Acquisition Process?
E. Jiménez-Ruiz1 , D. Hovland1 , L. Slaughter1,2 , T. Håndstad2 , A. Waaler1
1 2
Department of Informatics, University of Oslo, Norway Oslo University Hospital, Norway
1 Introduction
The work presented in this paper is framed within the context of the BigMed project, a
project funded by the Norwegian Research Council. One of the objectives of BigMed is
to enhance the phenotype acquisition process in newborns with a monogenetic disorder,
one of the four patient groups studied in the project. The use of the Human Phenotype
Ontology (HPO) [1] to tag phenotypes and systems like PhenoTips have substantially
contributed to the overall phenotype acquisition workflow. PhenoTips [2] is a system
for the acquisition of phenotypic information in patients with a genetic disease. Phe-
noTips also suggests, given a selected set of HPO terms, candidate diagnoses using
OMIM (Online Mendelian Inheritance in Man) codes, and related genes for a subse-
quent genetic test. Although PhenoTips represents a fantastic effort, we believe it could
be extended with suitable Semantic Web solutions. In this paper, we present the first
steps to adopt ontology alignment techniques to contribute to the diagnostic process.
2 Preliminary evaluation
We have conducted an evaluation about the coverage of links between phenotypes (HPO
terms) and diseases (OMIM terms) obtained using ontology alignment systems.
Reference datasets. We use as reference the phenotype annotation hpoteam.tab file
(we refer to this file as hpo-annotations) which contains annotations linking HPO terms
with OMIM (disease) terms.1 We also use the annotation file new phenotype (we refer
to this file as phenotips-annotations) used in PhenoTips as part of the Bayesian Ontology
Query Algorithm2 (BOQA) module to suggest diagnosis. From the hpo-annotations we
extracted 81,154 links (pairs hpo term-omim term), while 98,359 links were obtained
from the phenotips-annotations file.
Evaluation set-up. We have used the mappings computed by participating systems in
the Disease and Phenotype track [3] of the Ontology Alignment Evaluation Initiative [4]
(OAEI). The 2017 edition of the Disease and Phenotype track consists of four tasks.3
In this paper we focus on the results in the task involving the matching of the HPO and
OMIM ontologies. The first row in Table 1 shows the number of mappings computed
by the systems AML, LogMap, LogMapBio, DiSMatch, and mappings extracted from
BioPortal. All-Syst represents the union of all computed/extracted mappings.
The mappings computed in the Disease and Phenotype track represent phenotype-
to-phenotype correspondences (mostly equivalence) between HPO and OMIM terms.
?
Funded by the BIGMED and HealthInsight projects, and the SIRIUS Centre (no.: 237889).
1
http://compbio.charite.de/jenkins/job/hpo.annotations/
2
PhenoTips fork of BOQA: https://github.com/phenotips/boqa
3
http://oaei.ontologymatching.org/2017/phenotype/
2
Table 1. Computed mappings or links between HPO and OMIM terms.
Link Type AML LogMap LogMapBio DiSMatch BioPortal All-Syst
Phenotype-to-Phenotype 6,344 7,202 7,726 7,680 3,768 11,510
Phenotype-to-Disease 42,354 35,825 39,157 32,990 26,984 52,413
Table 2. Coverage of the links driven by the automatic ontology alignment systems.
hpo-annotations phenotips-annotations
System
Precision Recall Precision Recall
AML 0.880 0.459 0.848 0.349
LogMap 0.897 0.396 0.867 0.316
LogMapBio 0.889 0.429 0.860 0.342
DiSMatch 0.832 0.338 0.816 0.274
BioPortal 0.938 0.312 0.920 0.252
All-Syst 0.804 0.519 0.777 0.414
In order to obtain phenotype-to-disease links between HPO and OMIM terms, as in the
reference datasets presented above, we exploited the semantic information in OMIM
and the computed mappings. We extracted the associated OMIM disease terms for a
given OMIM phenotype term using the ontology properties manifestation of and inher-
itance type of. The second row in Table 1 shows the number of phenotype-to-disease
links derived from the phenotype-to-phenotype mappings.
Results and Discussion. Table 2 shows the coverage of the links derived from the auto-
matically computed alignments.4 The results in terms of Recall are not impressive and
only when considering all alignment sets we get near to 50% Recall. Giving a closer
look at the results, we noticed that a few OMIM ids present in the annotation files were
missing in the OMIM ontology.5 However, the missing OMIM ids are not significant
enough to explain the low Recall values. We also observed that a large number (i.e.,
>1,000) of HPO terms occurring in the links extracted from the annotation files were
not aligned to OMIM terms by any of the ontology alignment systems. There may be
three possible explanations: (i) the alignment systems fail to discover relevant corre-
spondences, (ii) the OMIM ontology does not cover all phenotype terms from the HPO
ontology, and/or (iii) the description of related diseases to a phenotype in the OMIM
ontology is limited. Future work aims at clarifying these possible limitations.
Although the results regarding Recall are not encouraging, the results with respect
to Precision may be considered promising since there are links derived by the automat-
ically computed correspondences that are not present in the annotation files. These new
links, however, require manual curation to assess their validity.
References
1. Köhler, S., et al.: The human phenotype ontology in 2017. Nucleic Acids Res. 45(D1) (2017)
2. Girdea, M., et al.: PhenoTips: Patient Phenotyping Software for Clinical and Research Use.
Human Mutation 34(8) (2013) 1057–1065
3. Harrow, I., Jimenez-Ruiz, E., et al.: Matching disease and phenotype ontologies in the ontol-
ogy alignment evaluation initiative. Journal of Biomedical Semantics (in press) (2017)
4. Achichi, M., et al.: Results of the Ontology Alignment Evaluation Initiative 2017. In: 12th In-
ternational Workshop on Ontology Matching (OM). (2017) http://oaei.ontologymatching.org/.
4
The coverage has been calculated in terms of Precision and Recall with respect to the (refer-
ence) link sets extracted from the hpo-annotations and phenotips-annotations files.
5
OMIM v.2016AB: https://bioportal.bioontology.org/ontologies/OMIM