Objective This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer – Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). Methods ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. Results To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infra-structure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

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Recent changes in data management within post-genomic clinical trials have emphasized the need for novel methods and tools to solve semantic and syntactic heterogeneities among distributed sources of information. ACGT is an Integrated Project funded by the European Commission that aims at building a GRID-based platform comprised by a set of tools to support multicentric post-genomic clinical trials on cancer. The main goal of ACGT is to provide seamless access to heterogeneous sources of information. For this purpose, two core tools were developed and included in the ACGT architecture: the ACGT Semantic Mediator (ACGT-SM), and the Data Access Wrappers (ACGT-DAWs). The ACGT-SM addresses semantics and schema integration, while the ACGT-DAWs cope with syntactic heterogeneities. Once the sources are bridged together, they can be seamlessly accessed using the RDQL query language.We tested our tools using a set of three relational and DICOM based image sources obtaining promising results.

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended “nanotype” to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other –omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

Managing large image collections has become an important issue for information companies and institutions. We present a cloud computing service and its application for the storage and analysis of very-large images. This service has been implemented using multiple distributed and collaborative agents. For image storage and analysis, a regionoriented data structure is utilized, which allows storing and describing image regions using low-level descriptors. Different types of structural relationships between regions are also taken into account. A distinctive goal of this work is that data operations are adapted for working in a distributed mode. This allows that an input image can be divided into different sub-images that can be stored and processed separately by different agents in the system, facilitating processing very-large images in a parallel manner. A key aspect to decrease processing time for parallelized tasks is the use of an appropriate load balancer to distribute and assign tasks to agents with less workload.