3810-193 Aveiro
Portugal
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José Luís Oliveiraassociate professor |
Universidade de Aveiro, DETI/IEETA
3810-193 Aveiro Portugal
(+351) 234 370 523
(+351) 234 370 545
jlo(_at_)ua.pt
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The projects’ ambition is the creation of a new set of solutions based in novel ICT technologies, developing a concept that encompasses the synergistic usage of cloud computing, with large database access and information retrieval, associated with advanced methods for reasoning and data mining (and with the basic scalable algorithms to support the dimensions of the data sets targeted).
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In recent years, the development and use of Electronic Healthcare Records (EHRs) throughout Europe has grown exponentially resulting in large volumes of clinical data. At the same time, large collections of disease‐specific data are recorded – in local, regional and/or national settings. This project combines the topic of generating a common patient health Information Framework (IF) with addressing the two Research Topics (RT’s) Obesity and its metabolic complications and Markers for the development of Alzheimer’s disease (AD) and other dementias.
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RD-Connect will develop a global infrastructure for sharing the research outputs of these and other rare disease projects, enabling scientists and clinicians worldwide to access a single centralized repository for omics data, phenotypic and biomaterial information. Every IRDiRC research project will be entitled to share its own data and access related data from other projects under policies agreed at a global level.
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The GEN2PHEN project has the overall ambition of unifying human and model organism genetic variation databases in such a way that the resulting holistic view of G2P data can be blended with all other biomedical database domains via one or more central genome browsers.
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The objective of this project is to develop a query expansion and document ranking method specially aimed at obtaining, from the MEDLINE database, a ranked list of publications that are most significant to a set of genes.
The overall goal is to instantiate a new network connectivity concept for medical imaging data and services at inter-institutional level. This will turn large volumes of clinical information and analytical tools, actually “locked” in clinical units, into shared repositories and high-quality collaborative environments for medical applications, education and research.
The overall objective of this project is the design, development and validation of a computerized system that exploits data from electronic healthcare records and biomedical databases for the early detection of adverse drug reactions.
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DNA Microarray technology is one of the most promising new technologies for global gene expression analysis. This technology is sophisticated, very expensive, highly interdisciplinary and produces vast amounts of data whose management and analysis pose significant challenges. This project aims to study new bi-clustering approaches that can help to obtain relevant information from gene expression microarrays.
In this project, software tools for data visualization and mathematical methodologies for identification of general rules governing RNA translation, and tools for mapping mRNA regions of high decoding error and for identifying putative gene expression regulatory sequences present in mRNAs, will be developed.
The INFOBIOMED network aims at setting a durable structure for the described collaborative approach at an European level, mobilising the critical mass and the resources necessary for enabling the collaborative approach that supports the consolidation of BMI as a crucial scientific discipline for future healthcare.
In this project, software tools for data visualization and mathematical methodologies for identification of general rules governing RNA translation, and tools for mapping mRNA regions of high decoding error and for identifying putative gene expression regulatory sequences present in mRNAs, will be developed.
INFOGENMED started in September 2002, (http://www.infogenmed.net), and the functionalities already built in the system allow for: (1) defining clinical pathways to guide the user in the navigation of multiple sources over the Internet; (2) identifying and characterizing the most relevant databases to support the molecular medicine practice for selected rare genetic diseases; (3) designing the integration methods, based on virtual databases, mediators and semantic vocabulary servers.