=Paper=
{{Paper
|id=None
|storemode=property
|title=A Protein Annotation Framework Empowered with Semantic Reasoning
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_poster_2.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/WuBSCM13
}}
==A Protein Annotation Framework Empowered with Semantic Reasoning==
https://ceur-ws.org/Vol-1035/iswc2013_poster_2.pdf
A Protein Annotation Framework Empowered
with Semantic Reasoning
Jemma X. Wu, Edmond J. Breen, Xiaomin Song,
Brett Cooke, and Mark P. Molloy
Australian Proteome Analysis Facility, Maquarie University, Sydney, Australia
{jwu,ebreen,xsong,bcooke,mmolly}@proteome.org.au
Abstract. This paper presents an association discovery framework for
proteins based on semantic annotations from biomedical literatures. An
automatic ontology-based annotation method is used to create a seman-
tic protein annotation knowledge base. A semantic reasoning service en-
ables realisation reasoning on original annotations to infer more accurate
associations. A case study on protein-disease association discovery on a
real-world colorectal cancer dataset is presented.
Keywords: Protein annotation, bioinformatics, semantic reasoning
1 Introduction
To bridge the gap between the biomedical science and bioinformatics, many
biomedical ontologies have been created in the past few years. Ontology-based
semantic annotation for biomedical entities are of interest to both biomedical
researchers and general public. Meanwhile, the biomedical domain has a large
and fast-growing amount of literature resources, among which MedLine1 is the
primary publication repository for biomedical research. Ontology-based biomed-
ical text annotation has shown promising progress and several tools have been
successfully developed and evaluated in biomedical text mining problems[2, 5, 4].
However, these generic text-based biomedical annotation tools only provide con-
cept level annotations. The ability to do protein-oriented semantic annotation
will greatly benefit the proteomics research by enabling easy protein association
discovery. Also, traditional text-based annotation tools tend to create excessive
annotations and some tools expand the raw annotations by using semantic rea-
soning[3]. Inferring of the most informative and accurate annotations will be
very valuable to efficient and accurate association discovery.
This paper proposes an integrated high performance framework that lever-
aging protein annotations and semantic reasoning to an informative protein-
biomedical concepts association Knowledge Base(KB). Starting from a list of
proteins, the system automatically retrieves a pool of MedLine citations and an-
notates the proteins using pre-defined biomedical ontologies. A realisation rea-
soning service is applied to infer more accurate protein association information.
In our preliminary study, the focus is on the discovery of potential protein-
disease associations. A case study on discovering protein-disease associations for
a real-world colorectal cancer tissue protein dataset is presented.
1
http://www.ncbi.nlm.nih.gov/pubmed
2 SPRAM: A Semantic PRotein Annotation framework
based on MedLine
We propose a Semantic PRotein Annotation method based on MedLine (SPRAM)
which produces semantically inferred protein annotations based on biomedical
literatures and ontologies (Fig.1). SPRAM starts with a list of proteins of in-
Fig. 1. The framework of Semantic Protein Annotation method based on MedLine.
terest. A list of mapped MedLine publication IDs (PMIDs) for each candidate
protein is produced by searching the UniProt2 and Entrez Gene3 databases. A
parallel process runs to execute PubMed article fetching, semantic annotation
and realisation reasoning based on biomedical ontologies, such as Human Dis-
ease Ontology (HDO4 ), for the pool of candidate PMIDs. The MedLine citations
retrieved by the PubMed’s Eutil service are further filtered by the co-occurrence
of protein names. We choose the BioPortal’s annotator service5 with no semantic
expanding as our annotator. The raw annotated concepts are post-processed by
a realisation reasoning service to generate a set of clean and accurate protein
annotations and inserted into the knowledge base. Biologists can then issue se-
mantic queries to retrieve all proteins which are associated with one or more
concepts in the ontology.
3 Semantic reasoning for protein annotations
In biomedical ontology annotations, very often an instance is annotated with
multiple classes with subclass relationships in the ontology. To the best of our
knowledge, existing biomedical annotation tools with semantic reasoning func-
tionalities only do semantic expanding[3]. There has been no prior work on
drilling down the annotations to most specific concepts by using the seman-
tic reasoning. Despite, in many cases, the most specific classes of a protein, can
more accurately represent their biomedical categorical information. For example,
the traditional protein Gene Ontology analysis that shows the distribution of bi-
ological process or molecular functions nearly always bias towards the top-level
classes in the ontology[1].
2
http://www.uniprot.org/
3
http://www.ncbi.nlm.nih.gov/gene
4
http://disease-ontology.org/
5
http://www.bioontology.org/wiki/index.php/Annotator Web service
We developed a specialised realisation reasoning service for dynamically gen-
erated annotations. Different to the traditional Description Logic most specific
concept reasoning, our algorithm works on a dynamic set of annotations on the
fly instead of assertions in a static KB. Only the most specific annotations will be
stored in the KB. The algorithm takes a set of semantic protein annotations, ,
0
and an ontology, O, that is based on. A most specific class set, , is initialised
to be an empty set, ∅. For each class t ∈ for each protein, find all subclasses of
0
t in O, i.e., {Ci } where Ci v t ∈ O. Class t is added to if {Ci } ∩ = ∅, i.e.,
t is the most specific annotation in given ontology O. The algorithm outputs
0
the most specific class set which will be inserted into the nascent KB.
Fig.2 shows an example of the effect of applying realisation reasoning on the
disease annotations for a protein with a UniProt ID “O43175”. Class “disease”,
“cancer” and “carcinoma” in the original annotation set are all realised to “ade-
nocarinoma” because the last concept is subsumed by those three concepts and
it is also in the original annotation set. This is important because it more accu-
rately represents the biomedical categorical information and reduces complexity.
Fig. 2. An example showing the change to the protein disease annotations after reali-
sation reasoning. Left: the original disease annotation. Middle: a simplified partial view
of Human Disease Ontology. Right: the disease annotation after realisation reasoning.
4 Case study: discovery of proteins-disease associations
for colorectal cancer tissues
Jankova et al. found 45 up-regulated proteins in colorectal cancer tissues by using
experimental protein iTRAQ analysis[1]. The biologists would like to know what
diseases are related to these proteins and if the associations to the colorectal
cancer have been discovered before.
To help biologists achieve these goals, we take these 45 proteins and use our
SPRAM workflow to assist discovering the potential diseases associated with
these proteins. The result was: 1080 MedLine citations, 354 diseases associations
based on HDO, that was reduced to 241 unique associations after realisation
reasoning. SPRAM returns a set of protein-disease association to the biologists.
That includes also the source reference titles and URLs. The biologist can then
use this result to help validate these associations easily by tracing back to the
references.
Fig.3 shows the changes of the distribution of the diseases associated with
these 45 proteins before and after realisation reasoning. The distribution after
realisation reasoning represents more accurate and sensible information. For ex-
ample, the top distributed concept, disease, was removed and the next, cancer,
was greatly reduced, thereby producing a clearer set of associations.
(a) DOA distribution before reasoning (b) DOA distribution after reasoning
Fig. 3. The comparison of the Disease Ontology Annotation (DOA) distributions be-
fore and after realisation reasoning for the 45 up-regulated colorectal cancer proteins
To find proteins reported as colorectal cancer related, the biologist issues
a query using the concept, “colorectal cancer”. The semantic reasoning service
rewrites this query into a union of this concept and all of its subclasses. The
result shows that 6 proteins (CEA, NNE, HSP 84, NPM, 3-PGDH and UEV-1)
reported in the literature as being related to colorectal cancer.
5 Conclusion
This paper proposes an automatic protein-oriented association discovery frame-
work based on semantic annotations from literature. A semantic reasoning ser-
vice provides realisation reasoning. We demonstrate the usage of our system on
protein-disease association discovery using a real-world colorectal cancer protein
dataset. In upcoming work, focus will be given to a ranking model of protein
associations and customisable selection of protein-PMID mappings.
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