=Paper=
{{Paper
|id=None
|storemode=property
|title=Extraction of Drug-Drug Interactions Using All Paths Graph Kernel
|pdfUrl=https://ceur-ws.org/Vol-761/paper10.pdf
|volume=Vol-761
}}
==Extraction of Drug-Drug Interactions Using All Paths Graph Kernel==
https://ceur-ws.org/Vol-761/paper10.pdf
EXTRACTION OF DRUG-DRUG
INTERACTIONS USING ALL PATHS
GRAPH KERNEL.
Shreyas Karnik1,2 , Abhinita Subhadarshini1,2 , Zhiping Wang2 , Luis M.
Rocha3,4 , and Lang Li*2,5
1
School of Informatics, Indiana University, Indianapolis, IN, USA, 46202
2
Center for Computational Biology & Bioinformatics, School of Medicine, Indiana
University , Indianapolis, IN, USA, 46202
3
School of Informatics & Computing, Indiana University, Bloomington, IN, USA,
47408
4
Instituto Gulbenkian de Ciencia, Oeiras, Portugal
5
Department of Medical & Molecular Genetics, School of Medicine, Indiana
University , Indianapolis, IN, USA, 46202
Corresponding Author: lali@iupui.edu
Abstract. Drug-drug interactions (DDIs) cause nearly 3% of all hospi-
tal admissions. Regulatory authorities such as the Food and Drug Ad-
ministration (FDA) and the pharmaceutical companies keep a rigorous
tab on the DDIs. The major source of DDI information is the biomedical
literature. In this paper we present a DDI extraction approach based on
all paths graph kernel [1] from the DrugDDI corpus [2]. We also evaluate
the method on an in-house developed clinical in vivo pharmacokinetic
DDI corpus. When the DDI extraction model was evaluated on the test
dataset from both corpora we recorded a F-score of 0.658 on the clinical
in vivo pharmacokinetic DDI corpus and 0.16 on the DrugDDI corpus.
1 Introduction
Polypharmacy has been a general clinical practice. More than 70% of old pop-
ulation (age >65) take more than 3 medications at the same time in US and
some European countries. Since more than 80% of the drugs on the market are
metabolized by the Cytochrome P450 enzyme system, and many of these drugs
are inhibitors and inducers of CYP450 enzyme system, drug interactions have
been extensively investigated in vitro and in vivo [3,4,5]. These DDIs in many
ways affect the overall effectiveness of the drug or at some times pose a risk of
serious side effects to the patients [6]. Thus, it becomes very challenging to for
the successful drug development and clinical patient care. Regulatory authori-
ties such as the Food and Drug Administration (FDA) and the pharmaceutical
companies keep a rigorous tab on the DDIs. Major source of DDI information
is the biomedical literature. Due to the unstructured nature of the free text in
the biomedical literature it is difficult and laborious process to extract and an-
alyze the DDIs from biomedical literature. With the exponential growth of the
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biomedical literature, there is a need for automatic information extraction (IE)
systems that aim at extracting DDIs from biomedical literature. The use of IE
systems to extract relationship among biological entities from biomedical liter-
ature has experienced success to a great scope [7] for example protein-protein
interaction extraction. Researchers have now started to investigate DDI IE from
biomedical literature. Some early attempts include retrieval of DDI relevant ar-
ticles from MEDLINE [8] ;DDI extraction based on reasoning approach [9]; DDI
extraction based on shallow parsing and linguistic rules[10];and DDI extraction
based on shallow linguistic kernel [2].
BioCreAtIvE has established the standard of evaluation methods and datasets
in the area of information extraction [7,11,12,13] which has been a asset for the
community. To encourage the involvement of the community in the DDI extrac-
tion Segura-Bedmar et al.[2] released an annotated corpus of DDIs (DrugDDI
corpus) from the biomedical literature and organized the DDIExtraction2011
challenge.
In this article, we implement the all paths graph kernel [1] to extract DDIs
from the DrugDDI corpus. We also test the all paths graph kernel approach on
in-house corpus that has annotations of pharmacokinetic DDIs from MEDLINE
abstracts.
The paper is organized as follows, section 2.1 and 2.2 describe the datasets,
section 2.3 describes the all paths graph kernel approach and section 3 describes
the results.
2 Methodology
DrugDDI Corpus We used the unified format [14] of the DrugDDI corpus
[2] of the DrugDDI corpus. Detailed description of the corpus can be found at
DrugDDI Corpus.
2.1 Clinical in-vivo pharmacokinetic DDI corpus
Our research group has been studying clinical DDIs reported in biomedical liter-
ature (MEDLINE abstracts) and extraction of numerical pharmacokinetic (PK)
data from them[15]. During this process, we have collected MEDLINE abstracts
that contain clinical PK DDIs, and further develop them into a PK DDI cor-
pus. We decided that the ultimate goal of this task is extraction of DDIs from
biomedical literature and it will be interesting to use this corpus as an additional
resource. This corpus comprises of 219 MEDLINE abstracts which contains one
or more of PK DDIs in same sentences. Here we call it PK-DDI corpus. Please
note that a PK DDI means that one drugs exposure is changed by the co-
administration of the other drug. As DrugDDI corpus focuses mainly on DDIs
that change drug effects, our PK-DDI corpus is a good complementary source. In
order to identify drugs in our PK-DDI corpus, we developed a dictionary based
tagging approach using all the drug name entries in DrugBank [16]. The corpus
was converted into the unified format as proposed in [14]. The DDI instances
([WUDFWLRQRIGUXJGUXJLQWHUDFWLRQVXVLQJDOOSDWKVJUDSKNHUQHO
were annotated based on guidelines from in-house experts. We split the corpus
into training (80%) and testing (20%) fractions. This corpus will also be made
public on the lines of the DrugDDI corpus. There are 825 true DDI pairs present
in our corpus.
2.2 All paths graph kernel
We implemented the approach described by Airola et al. [1] for DDI extrac-
tion. This approach centers around the drugs, where a graph representation of
the sentence is generated. Sentences are described as dependency graphs with
interacting components (drugs). The dependency graph is composed of two un-
connected sub-graphs: i) One sub-graph explores the dependency structure of
the sentence; ii) the other explores the linear order of the words in the sentence.
We used the Stanford parser [17]to generate the dependency graphs for both
corpora. IIn the dependency graph, the shortest path between two entities was
given higher weight as compared to other edges, this is because the shortest path
contains important keywords which are indicative of interaction between two en-
tities. In the linear sub-graph, all the edges have the same weight and the order
in which words occur before, in the middle, or after drug mentions was consid-
ered. The all paths graph kernel algorithm [18] was subsequently implemented to
compute the similarity between the graphical representations of the sentences.
In particular, all paths graph kernels will be generated for tagger positive DDI
sentences and negative DDI sentences. We then used Support Vector Machines
(SVM) for classification. More details about the all paths graph kernel algorithm
can be found in [1]. A pictorial representation of the approach is presented in
figure 1.
3 Results
In this study we used an in-house corpus in addition to the DrugDDI corpus;
both corpora contain training and testing subsets. We generated DDI extraction
models based on both the training datasets individually and combined, and
evaluated the performance of the DDI extraction models on the respective testing
datasets.
Table 1 illustrates the summary of training and testing data in two corpuses.
For the purpose of evaluation we used precision, recall and the balanced F-Score
measure. We also performed 10-fold cross-validation during the training phase.
Table 2 displays the DDI extraction performance on DDI-PK corpus testing
data. It suggests that using the DDI-PK corpus training data either with or
without the DrugDDI corpus training data, led to the precision above 0.78 and
recall above 0.64. On the other hand, if only the DrugDDI corpus was used, both
precision and recall were around 0.41.
Table 3 displays the DDI extraction performance on DrugDDI corpus testing
data. It suggests that all there models had similar perform in F-score, which was
between 0.13 and 0.16, although using DDI PK corpus generated slightly better
F-score than the other two approaches.
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Fig. 1. Description of the methodology
Dataset Number of sentences Number of DDI Pairs
PK DDI Corpus (Train) 1939 2411
PK DDI Corpus (Test) 498 606
DDI Corpus (Train) 3627 20888
DDI Corpus (Test) 1539 7026
Table 1. Summary of the corpora used in this study
([WUDFWLRQRIGUXJGUXJLQWHUDFWLRQVXVLQJDOOSDWKVJUDSKNHUQHO
Dataset F-Score Precision Recall
PK DDI Corpus (Train) + DDI Corpus (Train) 0.64 0.53 0.8
PK DDI Corpus (Train) 0.658 0.567 0.7857
DDI Corpus (Train) 0.415 0.417 0.414
Table 2. Performance of the different models on PK DDI Corpus (Testing dataset)
Dataset F-Score Precision Recall
PK DDI Corpus (Full) + DDI Corpus (Train) 0.1346 0.1250 0.1457
PK DDI Corpus (Full) 0.1605 0.1170 0.2556
DDI Corpus (Train) 0.1392 0.1187 0.1682
Table 3. Performance of the different models on DrugDDI corpus test data
4 Discussion and Conclusion
There is large room for improvement in the DDI extraction from the biomedical
literature. We also learned that the in-house DDI PK corpus and Drug DDI
corpus have different DDI structures. It seems the all paths graph kernel method
performed better in DDI PK corpus than the Drug DDI corpus.
The apparent low precision and recall in the Drug DDI corpus may result
from the fact that the number of real DDIs is much less than the number of
false DDIs in both corpus, but a comparison with the results of other teams is
forthcoming once those get released. It is also possible that the weights on the
sub-graph need to be further adjusted to get a better performance. We noticed
a marked performance difference between the training corpora. The sentences in
the DrugDDI corpus were long and complex. On the other hand, our DDI PK
corpus has a simply sentence structure, and there is an average of one to two
DDI pairs per abstract. Even with the same algorithm, these major differences
between two corpora resulted in different DDI extraction performances.
DrugDDI corpus focuses on DDIs that affect the clinical outcomes (i.e. phar-
macodynamics DDI); while PK DDI corpus focuses on DDIs that change the
drug exposure. They are complementary to each other. Therefore, our work en-
riches the set of resources and analysis available to this community.
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