M. Rakic, S. Nektarijevic, I. Milinkovic, G. Rakocevic, M. Bumbasirevic, V. Milutinovic, V. Lekovic (SRB)

withL. Luic (CRO) + M. Rudolf (SLO)

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Our Research Focus: 5.3.b (i+iii)   Development of ICT tools, services and specialized infrastructure for the bio-

medical researchers to support at least two of the following three activities: i) to share data and knowledge needed for a new integrative research approach in medicine (biomedical informatics), ii) to share or jointly develop multiscale models and simulators, iii) to create collaborative environments supporting this highly multidisciplinary field. When necessary, computing power and data management could be sought through access to existing advanced grid infrastructures as well as high performance computing resources such as the emerging petascale computing facilities. New tools, services and applications will also be evaluated on their effectiveness and their ability to interface with existing medical research infrastructures. Their targeted services will facilitate the clinical use of computer based organ and disease models as well as biomedical data. These tools and services will complement and be compatible with existing methods and standards (terminologies, ontologies, mark-up languages) like those used by the Network of Excellence –VPH NoE (FP7-ICT-call 2). International Cooperation in this field is encouraged. The objective is to support at least one IP to be funded under b).

  Our Competitive Advantage: Data Mining Infrastructure On Top of All

Above

Our approach concentrates on all issues underlined above, plus an added value of crucial importance: The infrastructure for data mining which enables a set of hypotheses to be defined and verified. Appropriate PoC implementations with contents from dentistry (with special emphasis on periodontal medicine) and orthopedics (with special emphasis on biomedics). Special emphasis research methodology oriented to rich statistical analyses.

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i (integrative) iii (collaborative)

Nota Benne: ii (interface) ii (interaction)

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Sharing data:Infrastructure for institutional networking

Sharing knowledge:DataMining + SemanticWeb + ConceptModeling

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Example #1: Interaction of periodontitis + diabetes mellitus+ multiple sclerosis ++

Example #2: Interaction of orthopedics + risk factors impact+ therapy success rates ++

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Special emphasis on reusing of infrastructure developed through former FP projects:

Special emphasis on eliminating the trapsof reusing (six different trap scenaria)

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Tentative list of core participants:

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Digital libraries include hidden knowledge Digital contents enable hypothesis testing

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Defining risk factors common to periodontitis and investigated diseases. Using the parameters which are according to contemporary science found to be significant for investigated diseases, we will screen and link observed data including risk factors, characteristics, and findings related to patients.

Investigating and defining the influence of periodontitis on the course of diseaseand on therapy success. By including systemic healthy patients with periodontitis, we will compare the clinical, immunological, microbiological and biochemical and other defined parameters between these systemic healthy and patients with MS and DM. Furthermore, by treating periodontitis of one part of ill patients, and by collecting post treatment specimens, we will determine the correlation between periodontal inflammation and systemic status.

Using obtained findings for better understanding still insufficiently explained pathogenesis and in improving the therapy plan.

Contributing to advancing and resolving the problem of three multifactor diseases,which are extremely wide spread and present in young population thus with great socioeconomic significance, by observing entire group of potential risk factors and indicators of disease.

Providing new perspective and ideas to scientists and physicians fighting against these diseases.

Creating a software trained on data, using most sophisticated contemporary techniques and chosen based on most recent and modern scientific achievements.

Implementing software for determining the risk of diseases and deterioration worsening of disease conditions.

Facilitating decisions relating treatment Helping in forecasting Improving knowledge

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Implementation Dissemination Analysis Beyond

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Collecting and tracking of risk factors for about 100 different use cases

Implementing the system and the procedures in 10 different medical institutions

Data sharing Knowledge mining (added value)

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TBD

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Objective 1: To generate use cases for hypotases testing (patient groups and problem types),

based on previous medical research experiences

of the entire consortium Objective 2: To specify contents and parameters to be tracked Objective 3: To design infrastructure with elements

of both computig and communications Objective 4: To develop a PoC and digital content for one

specific field, applicable to a statistically large enough test base

Objective 5: To select the datamining algorithms for analysis of test results.

Objective 6: To test the demo system in a number of specific clinical scenaria, in one country (e.g., SRB).

Objective 7: Same as above, in another country (e.g., CRO). Objective 8: Same as above, in a third country. (e.g., SLO) Objective 9: Final analysis and creation of recommendations

for clinical practice all over Europe. Objective 10: Dissemination to centers all over Europe.

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The project can be organized in 11 work packages:  ◦ WP0 Project management, RR◦ WP1 Definition of use case scenarios, X◦ WP2 Development of digital content, FRI◦ WP3 Development of system infrastructure, OPTILAB◦ WP4 Development of testing procedures, ETF◦ WP5 Development of datamining algorithms

for analysis of testing results, FHG ◦ WP6 Testing in environment A, SALERNO◦ WP7 Testing in environment B, FERRARA◦ WP8 Testing in environment C, KARLSTAD◦ WP9 Final analysis and preparation of recommendations

for clinical practice all over Europe, BSC◦ WP10 Dissemination, MAXELER

Specified work package leaders are tentative

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IP15

WANTED:

Creative Partners From the Region to

perform biomedical research to prove the concept,

so we can perform a huge statistical analysis,

andcover a large plethora of medical fields

in financially competitive environments

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