[Show abstract][Hide abstract] ABSTRACT: The eTUMOUR (eT) multi-centre project gathered in vivo and ex vivo magnetic resonance (MR) data, as well as transcriptomic and clinical information from brain tumour patients, with the purpose
of improving the diagnostic and prognostic evaluation of future patients. In order to carry this out, among other work, a
database—the eTDB—was developed. In addition to complex permission rules and software and management quality control (QC),
it was necessary to develop anonymization, processing and data visualization tools for the data uploaded. It was also necessary
to develop sophisticated curation strategies that involved on one hand, dedicated fields for QC-generated meta-data and specialized
queries and global permissions for senior curators and on the other, to establish a set of metrics to quantify its contents.
The indispensable dataset (ID), completeness and pairedness indices were set. The database contains 1317 cases created as
a result of the eT project and 304 from a previous project, INTERPRET. The number of cases fulfilling the ID was 656. Completeness
and pairedness were heterogeneous, depending on the data type involved.
Full-text · Article · Jan 2012 · Database The Journal of Biological Databases and Curation
[Show abstract][Hide abstract] ABSTRACT: Currently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we analyze the special security requirements for software support in health care and the HealthAgents system in particular. Our security solution consists of a link-anonymized data scheme, a secure data transportation service, a secure data sharing and collection service, and a more advanced access control mechanism. The novel security service architecture, as part of the integrated system architecture, provides a secure health-care infrastructure for HealthAgents and can be easily adapted for other health-care applications.
[Show abstract][Hide abstract] ABSTRACT: In this paper the authors describe the implementation of an ecologically inspired Digital Business Ecosystem (DBE) to promote and improve business collaboration by allowing Small and Medium sized Enterprises (SMEs) to find partners for interaction. Building on the analysis of how biologically-inspired computing is useful to represent business interactions and how small firms can benefit from this, the authors employ a methodology for defining ecological interactions among agents (SMEs) in a multi-agent system for the implementation of MADBE, an open-source Multi-Agent DBE. With MADBE, SMEs are able to publish their characteristics, services and needs and the system, using an intelligent protocol of interaction, simulates business interactions and suggests the optimal collaborative links between the participant firms that ultimately can derive in new joint ventures.
[Show abstract][Hide abstract] ABSTRACT: In this paper we propose MADBE, a Multi-Agent Digital Business Ecosystem. The purpose of this system is to provide a digital software environment for small organisations where they can interact and collaborate with each other and create new joint ventures. We believe that a multidisciplinary approach based on biology, computer science, and business concepts is necessary to produce an evolutionary self-organising system for networked business of small and medium sized enterprises (SMEs). In particular, we propose the first multi-agent digital business ecosystem based on ecological metaphors, which will enable us to define certain characteristics, based on real nature interactions and will also permit us to study the resulting network of businesses from an ecological perspective. An interaction-centred approach based on interaction protocols for knowledge sharing is adopted for the implementation of this system.
[Show abstract][Hide abstract] ABSTRACT: In this paper we employ an ecologically inspired simulation tool for creating and managing collaboration links in a digital business ecosystems. In open, self-organising environments such as those provided by digital ecosystems, in which digital entities behave autonomously for their own benefit, we are sometimes interested in finding collaboration links amongst agents in order to achieve goals in an easier way. A particular kind of this type of digital environments are the digital business ecosystems, in which companies interact with each other in order to create new joint ventures. We are interested in applying ecological ideas for the formation of links of collaboration between small companies represented by artificial intelligent agents within a digital business ecosystem. For this purpose we employ a simulation tool which takes inspiration from ecology to develop links of interactions between entities in a digital environment: this is a multi-agent based simulation environment which also facilitates the analysis of the resulting networks of interactions by employing a set of metrics that are commonly used for the study of complex systems. Analyses performed using this tool will allow us to manage the digital business ecosystem of small companies in an efficient manner.
[Show abstract][Hide abstract] ABSTRACT: This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent
decision support over a secure and distributed network of local databases or Data Marts. HealthAgents will not only develop
new pattern recognition methods for distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and
DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set
of new cases, based on a compatibility score. Using its Multi-Agent architecture, HealthAgents intends to apply cutting-edge
agent technology to the Biomedical field and develop the HealthAgents network, a globally distributed information and knowledge
repository for brain tumour diagnosis and prognosis.
[Show abstract][Hide abstract] ABSTRACT: This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing electronic healthcare records in a consistent manner. We present HADOM (HealthAgents domain ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of conceptual graphs (CGs). This allows to build upon the existing ontology permitting for modularity and flexibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval.