=Paper=
{{Paper
|id=Vol-2275/paper10
|storemode=property
|title=Cooperation of bio-ontologies for the classification of genetic intellectual disabilities : a diseasome approach.
|pdfUrl=https://ceur-ws.org/Vol-2275/paper10.pdf
|volume=Vol-2275
|authors=Gabin Personeni,Marie-Dominique Devignes,Malika Smail-Tabbone,Philippe Jonveaux,Céline Bonnet,Adrien Coulet
|dblpUrl=https://dblp.org/rec/conf/swat4ls/PersoneniDSJBC18
}}
==Cooperation of bio-ontologies for the classification of genetic intellectual disabilities : a diseasome approach.==
https://ceur-ws.org/Vol-2275/paper10.pdf
Cooperation of bio-ontologies for the
classification of genetic intellectual disabilities :
a diseasome approach
Gabin Personeni1 , Marie-Dominique Devignes1 , Malika Smaı̈l-Tabbone1 ,
Philippe Jonveaux2 , Céline Bonnet2 , and Adrien Coulet1,3
1
Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
2
Department of Genetics, Nancy University Hospital, Inserm U954, University of
Lorraine, Nancy, France
3
Stanford Center for Biomedical Informatics Research, Stanford University,
Stanford, California
Abstract. Bio-ontologies are widely used to annotate and characterize
biological objects or situations, enabling the use of shared or similar
features in classification tasks. It may appear beneficial to make two or
more bio-ontologies cooperate for building more complete descriptions,
and therefore more accurate classifications of biological objects. This
hypothesis is evaluated here for the classification of an heterogeneous
set of 374 Genetic Intellectual Disabilities (GIDs), using a diseasome
approach.
These GIDs are annotated with classes of the Human Phenotype Ontol-
ogy (HPO) and their causal genes with the three aspects of the Gene
Ontology (GO). We test two semantic similarity measures, and different
combinations of ontologies, to connect semantically similar diseases. We
then evaluate how well these ontologies, and their combinations, are ex-
ploited by the similarity measures to classify GIDs in accordance with
an expert classification.
Results show that combining the three aspects of GO achieves very good
overall performance, and that, for each GID class, a particular combi-
nation of 2 or 3 GO aspects and occasionally HPO yields the best per-
formance. These results illustrate how bio-ontologies can cooperate in a
classification by refining the characterization of biological objects.
Keywords: Semantic similarity · Bio-ontologies · Genetic Intellectual
Disabilities · Diseasome
1 Introduction
Bio-ontologies, such as the Gene Ontology (GO) [1] or the Human Phenotype
Ontology (HPO) [16] are used to annotate biological objects such as gene prod-
ucts or diseases, enabling their semantic comparison. In particular, there exists
a wide collection of semantic similarity measures, allowing to quantify the simi-
larity of objects with regard to their annotations [19, 3]. We investigate in this
article how several bio-ontologies can be used conjointly to cooperate to improve
classification of a heterogeneous set of Genetic Intellectual Disabilities (GID).
2 G. Personeni et al.
Numerous studies report on the hypothesis that analyzing disease networks,
here named diseasomes, may be a mean to discover new knowledge on mecha-
nisms or treatments of diseases [2]. Various methods for diseasome building have
been described in the literature. For instance, two diseases can be associated if
they share one causing gene [8], phenotype [10] or are linked through a chain
of protein-protein interactions [9]. Hoehndorf et al. [11] proposed a diseasome
that associates diseases with respect to their phenotypic similarity. They assem-
bled a dataset, extracted from the literature, of about 6, 000 diseases annotated
with their associated phenotypes, using classes of the Monarch Disease Ontol-
ogy (MonDO) [18]. The similarity of diseases with regard to their annotation
were subsequently computed with the SimGIC function [19]. We propose here
to extend this approach by conjointly using annotations taken from several on-
tologies, and by assessing their respective contribution to a disease classification
task.
The hypothesis that several bio-ontologies can somehow cooperate to refine
a diseasome is evaluated here within the task of classifying GIDs. The classifica-
tion of GIDs is of particular interest, and challenging for experts, because these
diseases are very heterogeneous both in terms of causal genes and clinical out-
comes. We focused on a set of 374 GIDs for which causal genes are known and
used for genetic diagnosis. We manually classified these diseases with experts into
five groups, on the basis of the biological mechanisms disturbed in the disease:
regulation, regulation of genetic expression, metabolic, synaptic, neurogenesis.
We detail in this article a diseasome approach based on semantic similarity of
GIDs at both the phenotype and genetic level, and study how it can match an
expert GID classification.
2 Material and Methods
2.1 Data and Ontologies
A dataset of 374 GIDs was built for this study on the basis of a list of 312
genes associated with GIDs derived from the work of Gilissen et al. [7] who ini-
tially compiled two lists of GID genes: a list of 528 “known” GID genes and
a list of 628 “candidates” genes, based on the number of reported patients in
which a mutation or variant of the gene is observed. The 312 genes retained
here (230 “known” genes and 82 “candidates”) are those that are found associ-
ated with a genetic disease in OMIM database (Online Mendelian Inheritance in
Man, http://omim.org) and used for diagnosis in the Genetics Laboratory of
Nancy Hospital. Four distinct ontologies were used in this study: Human Phe-
notype Ontology (HPO) [17] and the three aspects of Gene Ontology (GO) [1]
named here BP for Biological Process, CC for Cellular Component and MF for
Molecular Function. These three aspects of GO are organized into independent
hierarchies of classes related by the subsumption relation, and are here con-
sidered as separate ontologies. HPO annotations of GIDs were collected from
the HPO database (http://hpo.jax.org). BP, CC and MF annotations were
collected from the GOA database [12] at the European Bioinformatics Insti-
tute (https://www.ebi.ac.uk/GOA) for all UniProtKB proteins encoded by the
Cooperation of bio-ontologies for the classification of intellectual disabilities 3
genes associated to GIDs and transferred to the corresponding GID. The average
number of HPO classes associated per GID was 22.4 ± 17.5, whereas the average
numbers of BP, CC and MF classes per GID were 14.8 ± 16.4, 5.8 ± 4.3 and
5.2 ± 4, respectively. All GIDs could be associated with at least one HPO class
and one BP class. Only 27 GIDs were found lacking one or the other aspect of
GO annotation, mostly CC.
2.2 Expert classification of GIDs
GID diversity and heterogeneity renders their classification difficult. Our man-
ual classification is an attempt to integrate the state-of-the-art knowledge about
GIDs[13, 5, 15, 14] into the definition of five classes. The “Metabolic” class
represents diseases affecting synthesis or degradation of metabolites, leading
to metabolite deficiency or accumulation with deleterious consequences. The
“Synaptic” class represents diseases affecting the structure and the function of
synapses. The “Neurogenesis” class represents diseases affecting neuronal migra-
tion or proper development of central nervous system. The “Regulation of ge-
netic expression” class represents diseases in which genetic expression (chromatin
structure, transcription and its regulation, translation and post-translational
modifications) is affected. The “Regulation” class is for all other diseases in
which control of biological processes other than genetic expression is affected
(for instance transport of proteins or energetic balance of the cell). Our dataset
of 374 GIDs with their 312 responsible genes was manually distributed into these
five classes by expert inspection of their OMIM notices (disease and gene ones).
The resulting classification likely relies on several subjective arbitrary state-
ments, but it appeared sufficient for the methodology used in this study. Table
1 quantitatively describes the composition of each class of GIDs.
Table 1. Distribution of our GID dataset in the five classes of our manual classification.
Numbers of known genes refers to the list of Gilissen et al. [7].
Class GIDs Genes Known genes
Regulation 154 128 90(70%)
Regulation of genetic expression 70 57 38(67%)
Metabolic 105 96 78(81%)
Synaptic 31 25 17(68%)
Neurogenesis 33 21 17(81%)
All 374 312 230(74%)
The broad definition of each class leads to possible assignment of the same
disease and gene to two different classes. This is the case of 3, 5 and 10 GIDs
of the Metabolic, Synaptic and Neurogenesis classes, respectively that are also
classified in the Regulation class. One additional GID from the Neurogenesis
class is also classified as Regulation of genetic expression. This GID (OMIM
#613454: Rett syndrome, congenital form) illustrates the difficulty to classify
GIDs, as it is described as a severe neurodevelopmental disorder and therefore
classified in the Neurogenesis class, whereas its responsible gene is the FOXG1
4 G. Personeni et al.
gene, which codes for a repressor of the forked-head transcription factor family,
pointing to the Regulation of genetic expression class.
2.3 Semantic similarity measures
Semantic similarity measures quantify the proximity of two ontology classes, or
objects described by a set of classes, from an ontology. Such similarity mea-
sures may be used to build a diseasome, using disease annotations linked to an
ontology [11]. We applied such similarity-based diseasome approach to build a
diseasome of GIDs, on the basis of both their phenotypic and causal gene prod-
uct annotations. These annotations are expressed as classes from four ontologies
or ontologies fragments : HPO and the 3 aspects of Gene Ontology considered
separately — BP, CC and MF.
We aim at assessing the contribution of several ontologies to a diseasome, but
we also use two semantic similarity measures, to compare how different measures
behave with ontology combinations. First, we use a node-based semantic similar-
ity measure: SimGIC [19], which computes the ratio of common classes among
the ontology classes of two diseases, weighted by the information content of each
class, and considering all ancestors of each disease annotations. The informa-
tion content of a class, with respect to a dataset of annotations, is computed
as IC(x) = − log2 (P (x)), where P (x) is the probability that an object is anno-
tated with the class x. Higher values of IC denotes higher specificity of the class.
SimGIC was first introduced to compute similarity of genes annotated by GO
classes, and has been successfully used with MonDO to build a diseasome based
on phenotypic similarity [11]. Second, we use the edge-based similarity measure
IntelliGO [3], which permits to compare two biological objects by first computing
distances in the hierarchy between pairs of classes annotating the objects, and
then aggregating each pairwise similarities into a single-object similarity score.
As SimGIC, this aggregation step takes into account the information content of
the compared classes to weight their contribution to the similarity.
To assess the contribution of each ontology, we build several similarity func-
tions, using every possible combination of ontologies among BP, CC, MF and
HPO, combined with both IntelliGO and SimGIC. For this purpose and in a
first approach, we simply average the similarities computed separately with each
ontology. We thus test 15 possible combinations of one or more ontologies, in
turn combined with IntelliGO or SimGIC, resulting in 30 similarity functions.
2.4 Evaluation of similarity functions with respect to a reference
classification
Hoehndorf et al. described in [11] a methodology to evaluate the accuracy of a
similarity function with respect to a classification of diseases. This methodology
aims at verifying that the similarity function gives higher similarity scores to
pairs of diseases that belong to a same class of disease.
This evaluation is based on a Receiver Operating Characteristic (ROC) anal-
ysis, quantifying the accuracy of a binary classification model at varying degrees
of sensibility. In particular, a ranking of disease pairs, based on their similarity,
Cooperation of bio-ontologies for the classification of intellectual disabilities 5
serves as a classification model whose sensibility can be adjusted by defining a
threshold of similarity above which pairs of diseases are labeled as positive by
the model. The ROC curve represents the true positives rate as a function of
the false positive rate. The ROC Area Under the Curve or ROCAUC can be
computed from such a curve, and represents the probability for a random pair
of diseases from the positive class to have a higher similarity than a random pair
of diseases from the negative class. We note ROCAU C(R, P ) the function that
computes the ROCAUC given a ranking R, and the set of positive elements P ,
describing which elements of R are to be considered positive for the purpose of
the evaluation.
The ROCAUC-based evaluation can be conducted either for each single class
of diseases or for the entire classification of diseases. This evaluation can also be
performed with a classification of diseases in which a disease can belong to more
than one class.
Data: The set of diseases D, a similarity between diseases
sim : D × D → R+
Result: Average ROCAUC for all diseases
begin
ROCAU Cavg = 0
foreach disease d ∈ D do
ranking ← x ∈ (D − {d}) ranked in descending order of sim(d, x)
pos ← {x ∈ D | d and x share a disease class}
ROCAU Cavg ← ROCAU Cavg + ROCAU C(ranking, pos)
end
return ROCAU Cavg /|D|
end
Algorithm 1: Evaluation algorithm for a similarity function sim on a classi-
fication task with several overlapping classes of diseases.
Data: The set of diseases D, a disease class C, a similarity between
diseases sim : D × D → R+
Result: Average ROCAUC for diseases of C
begin
ROCAU Cavg = 0
pos ← {x ∈ D | x has class C}
foreach disease d in class C do
ranking ← x ∈ (D − {d}) ranked in descending order of sim(d, x)
ROCAU Cavg ← ROCAU Cavg + ROCAU C(ranking, pos)
end
return ROCAU Cavg /|DC |
end
Algorithm 2: Evaluation algorithm for a similarity function sim on a classi-
fication task with respect to a single class of diseases C.
6 G. Personeni et al.
The Algorithm 1 describes how the evaluation is performed globally on all
disease classes, for an arbitrary similarity function sim. For each disease d of
the dataset, we compute the ranking of the other diseases using sim, from the
most similar to the least similar. Here, we want high-ranking diseases to share a
disease class with d, thus, we consider the positive class to be the set of diseases
sharing a GID class with d. The ranking is then evaluated by computing the
ROCAUC for the prediction on that positive class. ROCAUCs for each disease
in the dataset are then averaged to obtain a global evaluation score.
The Algorithm 2 describes how a similarity function sim is evaluated with
respect to a single disease class, noted C. For each disease d of the GID class
C, we compute a ranking of the other diseases using sim, and we define the
positive class to be the set of diseases that belong to C. Again, the ranking is
then evaluated by computing the ROCAUC for the prediction on that positive
class. ROCAUCs for each disease of the class are then averaged to obtain a score
representing how well the similarity function reflects this class.
3 Results
We apply the methodology described previously to our set of 374 GID. This set
of diseases has phenotypic annotations expressed as HPO classes and the genetic
annotations of their causing genes, expressed as GO classes and split into the
three aspects of GO considered here as three independent ontologies. Indeed, as
their class hierarchy are separate, semantic similarity measures such as IntelliGO
and SimGIC cannot compare classes from these different aspects.
We computed all pairwise similarities on our set of GIDs, for all 30 possible
similarity functions resulting from the combinations of the four ontologies (BP,
CC, MF and HPO) and the two semantic similarity measures (IntelliGO and
SimGIC). Each of these similarity functions was then evaluated by computing
the average ROCAUC for each of the 5 GID classes, as described in Algorithm
2, and for all classes considered together, as described in Algorithm 1.
Tables 2 and 3 present a selection of the results of theses evaluations for differ-
ent similarity functions based on different combinations of ontologies, computed
using IntelliGO and SimGIC respectively. Results are given for 6 classification
tasks, one task for each GID class evaluated separately, as described in Algo-
rithm 2, and a sixth task evaluating the performance of the similarity function
on all 5 GID classes, as described in Algorithm 1. For each task, we tested every
possible combination of ontologies from BP, CC, MF and HPO, but both Tables
report only results for combinations of interest: single ontologies, all GO aspects,
all ontologies, and all combinations that provide the best performance on any
task for either SimGIC or IntelliGO.
Unsurprisingly, the results are highly variable depending on which ontolo-
gies are considered, and which similarity measure is used, and they also vary
across different classification tasks. In particular, we observe that HPO does not
positively contribute to the performance when combined with other ontologies,
with the exception of the classification task of the Neurogenesis class. We also
note that similarity functions using only HPO have poor performance compared
with those using a single GO aspect in most cases, although such a function
Cooperation of bio-ontologies for the classification of intellectual disabilities 7
performs better than a random classifier. This suggests that, for classes other
than Neurogenesis, HPO does not bring more information compared to GO than
noise. However, combining several GO aspects produces a great increase in per-
formance, which is notably visible for the Regulation of Genetic Expression class:
IntelliGO performance increases from 0.740 in the best case with only one GO
aspect to 0.803 with all three of them, and SimGIC performance increases from
0.905 to 0.936.
Table 2. ROCAUC obtained using IntelliGO to classify GIDs into 5 classes (Reg-
ulation, Regulation of Genetic Expression, Metabolic, Synaptic, Neurogenesis) with
different combinations of ontologies among the three aspects of GO (BP, CC and MF)
and the phenotype ontology HPO. For each GID class, an average ROCAUC is also
computed as described in the Algorithm 2. An average ROCAUC is computed across
all classes as described in the Algorithm 1. Values in bold denote the maximum average
ROCAUC for each evaluation task (each single class or all classes). For each task, the
Table presents the threshold at which the performance score is significant (p < 0.01),
which was computed empirically using random similarity functions.
Classes Reg. Reg. GE Metab. Synap. Neuro. All
Class size 154 70 105 31 33 374
Threshold(p<0.01) 0.507 0.514 0.511 0.533 0.531 0.506
BP 0.509 0.681 0.838 0.711 0.548 0.652
CC 0.532 0.719 0.681 0.723 0.563 0.627
MF 0.526 0.740 0.749 0.628 0.610 0.642
HPO 0.481 0.584 0.608 0.530 0.590 0.548
BP, CC, MF 0.526 0.803 0.862 0.732 0.595 0.693
BP, CC, MF, HPO 0.520 0.787 0.854 0.720 0.625 0.687
BP, CC 0.512 0.750 0.844 0.772 0.547 0.673
BP, MF 0.523 0.768 0.856 0.690 0.611 0.681
CC, HPO 0.499 0.684 0.691 0.677 0.582 0.608
MF, HPO 0.515 0.732 0.743 0.620 0.657 0.638
Maximum 0.532 0.803 0.862 0.772 0.657 0.693
If combining all three aspects of GO provides the best overall performance,
we nonetheless observe that this is not necessarily the best combination for the
classification task on each individual GID class :
– The Regulation class is poorly predicted by most similarity functions. In par-
ticular, similarity functions based on HPO alone are not performing better
than a random classification.
– The Regulation of Genetic Expression class is best when computed using
SimGIC (0.936) and considering the three aspects of GO conjointly. How-
ever, in this case, considering HPO on top of GO does not offer any increase
in performance. Similarly, IntelliGO performs best on this classification task
when considering the three aspects of GO, but shows a decrease in perfor-
mance when also considering HPO.
– The Metabolic class is best predicted using IntelliGO (0.862) when using all
three aspects of GO. However, we observe that SimGIC (0.785) obtains its
8 G. Personeni et al.
Table 3. ROCAUC obtained using SimGIC to classify GIDs into 5 classes (see Table
2 for details).
Classes Reg. Reg. GE Metab. Synap. Neuro. All
Class size 154 70 105 31 33 374
Threshold(p<0.01) 0.507 0.514 0.511 0.533 0.531 0.506
BP 0.523 0.905 0.785 0.758 0.568 0.691
CC 0.515 0.846 0.659 0.787 0.561 0.641
MF 0.507 0.830 0.735 0.588 0.560 0.641
HPO 0.502 0.574 0.574 0.559 0.594 0.548
BP, CC, MF 0.527 0.936 0.764 0.7911 0.581 0.695
BP, CC, MF, HPO 0.524 0.933 0.748 0.768 0.628 0.691
BP, CC 0.525 0.927 0.750 0.819 0.578 0.690
BP, MF 0.522 0.916 0.785 0.711 0.571 0.688
CC, HPO 0.514 0.817 0.662 0.757 0.633 0.640
MF, HPO 0.513 0.813 0.643 0.625 0.616 0.622
Maximum 0.527 0.936 0.785 0.819 0.633 0.695
best performance score on this classification task when considering only BP
and MF. However, this divergence between the two measures is small, as
IntelliGO performs almost as well with only BP and MF (0.856).
– The Synaptic class is best predicted when using only the BP and CC aspects
of GO for both SimGIC (0.819) and IntelliGO (0.772).
– The Neurogenesis class is the only class for which we obtain better results
when adding HPO to the aspects of GO, for both IntelliGO and SimGIC.
In particular, IntelliGO gets a performance score of 0.657 when using MF
and HPO (compared to 0.610 with MF alone, or 0.590 with HPO alone),
while SimGIC gets a performance score of 0.633 (compared to 0.561 with
CC alone, or 0.594 with HPO alone).
4 Discussion and Conclusion
We study in this article how different ontologies can cooperate to improve how a
diseasome can reflect the expert knowledge of a classification of GID. We evaluate
here two semantic similarity measures, IntelliGO and SimGIC on a classification
task realized with 5 GID classes, using different combinations of phenotypic and
genetic ontologies. The results show that phenotypic annotations from HPO are
not sufficient to reflect our expert classification, while genetic annotations from
each aspect of GO can offer better performance. Furthermore, combining several
aspects of GO further improve performance and, for the Neurogenesis GID class,
combining a GO aspect with HPO offer the best performance.
This illustrates that the cooperation of several ontologies is suitable for such a
classification task and can improve a diseasome approach. However, the relevance
of each ontology greatly depends on individual disease classes, and considering
too many ontologies may have a negative effect. This constitutes a limitation of
the use of semantic similarity measures, in that they are sensitive to the quality
of annotations, as well to annotations irrelevant to class to predict. The results
obtained show that overall, considering only the 3 aspects of GO yields often
Cooperation of bio-ontologies for the classification of intellectual disabilities 9
slightly better classification performance than considering both HPO and GO.
Here, the contribution of HPO to the classification performance may be limited
by the GID dataset we used, as these diseases may be too difficult to distinguish
based on their phenotypes alone, as other studies show that HPO is suitable to
classify more phenotypically heterogeneous sets of diseases [11]. It seems that
in such cases, deciding on which ontologies to consider requires an iterative
empirical approach that consider all possible combinations of ontologies. In fact,
because performances do not necessarily increase with the number of ontologies,
proposing a non-exhaustive strategy to select the best combination of ontologies
for a particular task is not trivial.
Furthermore, it may be necessary to develop more sophisticated methods
for aggregating similarities based on different ontologies. Here, we used an un-
weighted average of similarities each using a different ontology. Weighting the
contribution of each ontology to the aggregated similarity could be done in sev-
eral ways, for instance, by considering the number of annotations in each on-
tology for the compared disease, or by empirically determining an appropriate
weighting scheme, rather than including or excluding ontologies. Moreover, as
we observe that different settings and therefore different diseasome models are
optimal only for certain classes, ways to aggregate different models could be
explored, such as boosting algorithms [6] or bagging predictors [4].
Diseasomes based on semantic similarity are able to reflect an expert clas-
sification of diseases, as illustrated in the different classification experiments
presented in this article. Such a diseasome can be used to classify new diseases
by simple propagation of the neighboring diseases classes. Here, the cooperation
of several biomedical ontology was shown to be relevant in many cases, how-
ever selecting the right ontologies to consider for a particular task require some
trial and error. We note that both semantic similarity measures, IntelliGO and
SimGIC, have varying performance on the different classification tasks presented
in this article: some GID classes seem to be better predicted by one of these two
measures. However, these differences do not permit to conclude that one of these
measures perform strictly better than the other, as their overall performances
are very similar. In summary, semantic similarity measures with various com-
binations of ontologies allow to propose a diseasome as a model synthesizing
descriptions of GIDs in regards with several ontologies, in good agreement with
an expert classification of such diseases. Such cooperation of bio-ontologies could
also be explored with other machine learning methods.
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