Lecture Notes in Computer Science 6865
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Nadia Pisanti
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Preface
Biomedical engineering and medical informatics represent challenging and rapidly

¨
ohm
Sami Khuri
Lenka Lhotsk´a
Nadia Pisanti
class="bi x0 y24 w1 h1"
Organization
General Chair
Christian B
¨
ohm University of Munich, Germany
Program Chairs
Sami Khuri San Jos´e State University, USA
Lenka Lhotsk´a Czech Technical University Prague,
Czech Republic
Nadia Pisanti University of Pisa, Italy
Poster Session Chairs
Vaclav Chudacek Czech Technical University in Prague,
Czech Republic
Roland Wagner University of Linz, Austria
Program Committee
Werner Aigner FAW, Austria
Fuat Akal Functional Genomics Center Zurich,
Switzerland
Tatsuya Akutsu Kyoto University, Japan
Andreas Albrecht Queen’s University Belfast, UK
Julien Allali LABRI, University of Bordeaux 1, France
Lijo Anto University of Kerala, India
Rub´en Arma˜nanzas Arnedillo Technical University of Madrid, Spain
Peter Baumann Jacobs University Bremen, Germany

Sami Khuri San Jose State University, USA
Michal Kr´atk´y Technical University of Ostrava,
Czech Republic
Josef K
¨
ung University of Linz, Austria
Gorka Lasso-Cabrera CIC bioGUNE, Spain
Marc Lensink ULB, Belgium
Lenka Lhotsk´a Czech Technical University, Czech Republic
Roger Marshall Plymouth Ystate University, USA
Elio Masciari ICAR-CNR, Universit`a della Calabria, Italy
Henning Mersch RWTH Aachen University, Germany
Aleksandar Milosavljevic Baylor College of Medicine, USA
Jean-Christophe Nebel Kingston University, UK
Vit Novacek National University of Ireland, Galway, Ireland
Nadia Pisanti University of Pisa, Italy
Cinzia Pizzi Universit`a degli Studi di Padova, Italy
Clara Pizzuti Institute for High Performance Computing and
Networking (ICAR)-National Research Council
(CNR), Italy
Meikel Poess Oracle Corporation
Hershel Safer Weizmann Institute of Science, Israel
Nick Sahinidis Carnegie Mellon University, USA
Roberto Santana Technical University of Madrid, Spain
Kristan Schneider University of Vienna, Austria
Jens Stoye University of Bielefeld, Germany
A Min Tjoa Vienna University of Technology, Austria
Paul van der Vet University of Twente, The Netherlands
Roland R. Wagner University of Linz, Austria
Oren Weimann Weizmann Institute, Israel

Dong Gyu Le e, Jang-Whan Bae, Ho Sun S hon, and Keun Ho Ryu
Monitoring of Physiological Signs Using Telemonitoring System 66
Jan Havl´ık, Jan Dvoˇr´ak, Jakub Par´ak, and Lenka Lhotsk´a
Workflow Management and Decision Support in
Medicine
SciProv: An Architecture for Semantic Query in Provenance Metadata
on e-Science Context 68
Wander Gaspar, Reg ina Braga, and Fernanda Campos
Integration of Procedural Knowledge in Multi-Agent Systems in
Medicine 82
Lenka Lhotsk´a, Branislav Bosansky, and Jaromir Dolezal
XII Table of Contents
A Framework for the Production and Analysis of Hospital Quality
Indicators 96
Albe rto Freitas, Tiago Costa, Bernardo Marques, Juliano Gaspar,
Jorge Gomes, Fernando Lopes, and Isabel Lema
Process Analysis and Reengineering in the Health Sector 106
Antonio Di Leva, Salvatore Femiano, and Luca Giovo
Classification in Bioinformatics
Binary Classification Models Comparison: On the Similarity of Datasets
and Confusion Matrix for Predictive Toxicology Applications 108
Mokhairi Makhtar, Daniel C. Neagu, and Mick J. Ridley
Clustering of Multiple Microarray Experiments Using Information
Integration 123
Elena Kostadinova, Veselka Boeva, and Niklas Lavesson
Data Mining in Bioinformatics
A High Performing Tool for Residue Solvent Accessibility Prediction 138
Lore nzo Palmieri, Maria Fe derico, Mauro Leoncini, and
Manuela Montangero
Removing Artifacts of Approximated Motifs 153

ing techniques. Connecting orthology information to the genes that cause
genetic diseases, such as hereditary cancers, may produce fruitful results
in translational bioinformatics thanks to the integration of biological and
clinical data. Clusters of orthologous genes are sets of genes from different
species that can be traced to a common ancestor, so they share biological
information and therefore, they might have similar biomedical meaning
and function.
Linking such information to medical decision support systems
would permit physicians to access relevant genetic information, which is
becoming of paramount importance for medical treatments and research.
Thus, we present the integration of a commercial system for decision-
making based on cancer treatment guidelines, ONCOdata, and a semantic
repository about orthology and genetic diseases, OGO. The integration of
both systems has allowed the medical users of ONCOdata to make more
informed decisions.
Keywords: Ontology, Translational bioinformatics, Cluster of Orthologs,
Genetic Diseases.
1 Introduction
Translational bioinformatics is involved in the relation of bioinformatics and clin-
ical medicine. Bioinformatics was originated by the outstanding development of
information technologies and genetic engineering, and the effort and investments
during the last decades have created strong links between Information Technol-
ogy and Life Sciences Information technologies are mainly focused on routine
and time-consuming tasks that can be automated. Such tasks are often related
to data integration, repository management, automation of experiments and the
assembling of contiguous sequences. On the medical side, decision support sys-
tems for the diagnosis and treatment of cancers are an increasingly important
factor for the improvement of medical practice [1][2][3][4]. The large amount of
C. B¨ohm et al. (Eds.): ITBAM 2011, LNCS 6865, pp. 1–15, 2011.
c

The research work described in this paper extends a commercial decision-
making system on cancer treatments, the ONCOdata system [10], which has been
used in the last years in a number of oncological units in Spain. In silico studies
of the relationships between human variations and their effect on diseases have
be considered key to the development of clinically relevant therapeutic strategies
[11]. Therefore, including information of the genetic component of the diseases
addressed by the professionals who are using ONCOdata was considered crucial
for adapting the system to state-of-the-art biomedical challenges.
To this end we have used the OGO system [12], which provides integration
information on clusters of orthologous genes and the relations between genes
and genetic diseases. Thus, we had to develop methods for the exchange of
information between two heterogeneous systems. OGO is based on semantic
technologies whereas ONCOdata was developed using more traditional software
technologies, although it makes use of some expert knowledge in the form of
rules and guidelines.
The structure of the rest of the paper is described next. First, the back-
ground knowledge and the description of the systems used for this translational
Translational Bioinformatics for Decision-Making on Cancer Treatments 3
experience are presented in Section 2. Then, the method used for the exchange
of information in Section 3, whereas the results will be presented in Section 4.
Some discussion will be provided in Section 5. Finally, the conclusions will be
put forward in Section 6.
2 Background
The core of this research project comprises the two systems that will be in-
terconnected after this effort. On the one hand, ONCOdata is a commercial
system that supports medical doctors on decision-making about cancer treat-
ments. Thus, it is an intelligent system which facilitates decision-making based
on medical practice and medical guidelines. On the other hand, OGO provides
an integrated knowledge base about orthology and hereditary genetic diseases.
OGO uses Semantic Web Technologies for representing the biomedical knowledge

on their medical and pathological information. Then, its reasoning engine uses
this representation and a set of expert rules to generate the recommendations.
1

2

3

4J.A.Mi˜narro-Gim´enez et al.
The knowledge base used by the reasoning engine was developed by a group
of cancer domain experts and knowledge management experts, which acted as
consultants for the company. The knowledge base was technically built by using
Multiple Classification Ripple Down Rules[14]. Besides, the development and
maintenance of the knowledge base follows an iterative and incremental process.
Physicians use ONCOdata through a web interface that allows them to insert
the patient’s medical information, and then to retrieve the recommendations
about the suitable treatment. Not only recommendations are provided, but also
the evidences and bibliographic materials that support those recommendations.
Thus, physicians, after gathering information from cancer patients, can find
medical advice from the ONCOdata system which facilitates making the de-
cision on cancer treatment. This process is described in Figure 1.
Doctor
Treatment
Recommendations
Fill out
P
atient’s
Health
R
ecord

Process
Admission
Pathology
Monitoring
Radiology
Monitoring
Medical
Oncology
Surgery
Breast
Cancer
Unit
Pathology
Radiology
Fig. 2. The Breast Cancer Process
orthology. Then, information sources about genetic diseases were also integrated
to covert it in a translational resource. The OGO system provides information
about orthologous clusters, gene symbols and identifiers, their organism names
and their protein identifiers and accession numbers, genetic disorders names,
the genes involved in the diseases, their chromosome locations and their related
scientific papers.
The information contained in the OGO system is retrieved from the
following publicly available resources: KOGs
4
, Inparanoid
5
, Homologene
6
,Or-
thoMCL

orthologous genes and genetic diseases. This ontological knowledge base is then
populated through the execution of the data integration process. The proper
semantic integration is basically guided by the global ontology. The definition
of the OGO ontology also includes restrictions to avoid inconsistencies in the
OGO KB. The restrictions defined in the ontology were basically disjointness,
existential qualifiers (to avoid inconsistencies in the range of object properties);
and cardinality constraints. The Jena Semantic Web Framework
12
is capable of
detecting such issues, therefore its usage facilitates checking the consistency of
the ontology when used together with reasoners, such as Pellet
13
.TheOGOKB
contains more than 90,000 orthologous clusters, more than a million of genes,
and circa a million of proteins. Besides, from the genetic diseases perspective it
contains approximately 16,000 human genetic disorders instances and more than
17,000 references to scientific papers.

causedBy
connectedTo
hasMethod
has Disorder
Reference
related
Articles
Location
hasOrthologous
has Resource
isTranslatedTo
belongsToOrganism

13
/>Translational Bioinformatics for Decision-Making on Cancer Treatments 7
3 Information Exchange between ONCOdata and OGO
In this section we describe the scope of the exchange information between the sys-
tem and the details of how the communication process has been developed. First,
we describe the approach followed in this work for establishing the communication
between both systems. Second, we describe how the OGO system makes available
its KB to external applications. Third, we describe how the ONCOdata system
exploits the OGO KB functionalities. Finally, we describe the technical details of
the communication module and its query interfaces and evaluate the results.
3.1 The Approach
As it has been aforementioned, ONCOdata and OGO are two completely sepa-
rate applications, thus a solution with minimum coupling between the systems
was required. Several technologies for interoperability between applications, such
as XML-RPC
14
,RMI
15
,CORBA
16
or Web Services
17
, were evaluated. This
evaluation pointed out that the features of web services are the most suitable for
the project requirements. Web services provide loosely coupled communication,
and text-encoded data and messages. The widespread adoption of SOAP
18
and
WSDL
19

/>8J.A.Mi˜narro-Gim´enez et al.

OGO
Repository

Domain
Ontology
Web
server
Client
app
ONCOdata
decision
ONCOdata
register
ONCOdata
Fig. 4. The Integration Scenario
to achieve this goal: (1) service for querying orthology information by using
gene names and its corresponding organism; (2) service for querying information
about genetic diseases by using disease names; and (3) service for querying the
OGO knowledge base by using user-defined SPARQL queries. OGO sends the
results in XML documents, whose structure depends on the service that was
invoked:
– Orthology information: the returned document will consist of all the genes

tient’s medical history from ONCOdata record. If the case study contains any
hereditary risk of cancer, ONCOdata decision seeks for the breast cancer disease
information from the OGO system. As a result of this service invocation, the
information of the different diseases and the corresponding genes is retrieved.
Next, ONCOdata infers and shows the treatment recommendationsaswellas
the biomedical information associated with the disease. Finally, the physician
selects a treatment, which is then recorded in the patient’s clinical record using
ONCOdata record.
Admission
Radiology
Pathology
Breast Cancer Unit
Breast Cancer Process

ONCOdata
register
Case Study
Treatements

ONCOdata

decision
OGO
KB
Disease Name
Information of
Diseases
Fig. 5. The ONCOdata module
4Results
As mentioned, ONCOdata can query the OGO knowledge base by invoking

variables used in the query pattern, which is shown in Figure 8, represent the
relationships and properties related to the disease class of the OGO ontology.
Thus, the element nodes of the XML document are the data of the query variable.
The root nodes of the XML document correspond to disease instances, and their
child nodes correspond to their relationships and properties. Figure 9 shows
an excerpt of the returned XML document which is generated when seeking
for information on breast cancer. Finally, the XML document is processed by
ONCOdata and displayed to the user.
The resulting system has been validated by the medical consultant of the
company. For this purpose, a series of tests were designed by them and were
systematically executed. They did validate that the new information was really
useful from the medical perspective to support clinical practice.
Translational Bioinformatics for Decision-Making on Cancer Treatments 11
Fig. 7. The WSDL document for querying on genetic diseases
Fig. 8. The SPARQL query pattern used for querying on genetic disease information
12 J.A. Mi˜narro-Gim´enez et al.
Fig. 9. Excerpt of the XML document returned when seeking for breast cancer disease
5 Discussion
Decision support systems play an increasingly important role to assign medical
treatments to patients. Such systems increase the safety of patients by preventing
medical errors, and facilitate decision-making processes by reducing the time in
seeking for the most appropriate medical treatment.
In this way, ONCOdata is a decision-making system for the allocation of
cancer treatments based on evidences. The rules used by ONCOdata for decision-
making purpose were drawn from clinical guidelines. These guidelines do not
make use of patient’s biomedical information, so the decisions about treatments
are made without taking individual issues not included in the clinical records
into account. However, such additional information is considered by professionals
as important for improving the quality and the safety of the care they deliver
to the patients. This goal is addressed in this work by translational research