From access and integration to mining of secure genomic data sets across the grid

The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled Microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS

E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury

Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented

Secure, reliable and dynamic access to distributed clinical data

An abundance of statistical and scientific data exists in the area of clinical and epidemiological studies. Much of this data is distributed across regional, national and international boundaries with different policies on access and usage, and a multitude of different schemata for the data often complicated by the variety of supporting clinical coding schemes. This prevents the wide scale collation and analysis of such data as is often needed to infer clinical outcomes and to determine the often moderate effect of drugs. Through grid technologies it is possible to overcome the barriers introduced by distribution of heterogeneous data and services. However reliability, dynamicity and fine-grained security are essential in this domain, and are not typically offered by current grids. The MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) has implemented a prototype infrastructure specifically designed to meet these challenges. This paper describes this on-going implementation effort and the lessons learned in building grid frameworks for and within a clinical environment

Supporting UK-wide e-clinical trials and studies

As clinical trials and epidemiological studies become increasingly large, covering wider (national) geographical areas and involving ever broader populations, the need to provide an information management infrastructure that can support such endeavours is essential. A wealth of clinical data now exists at varying levels of care (primary care, secondary care, etc.). Simple, secure access to such data would greatly benefit the key processes involved in clinical trials and epidemiological studies: patient recruitment, data collection and study management. The Grid paradigm provides one model for seamless access to such data and support of these processes. The VOTES project (Virtual Organisations for Trials and Epidemiological Studies) is a collaboration between several UK institutions to implement a generic framework that effectively leverages the available health-care information across the UK to support more efficient gathering and processing of trial information. The structure of the information available in the health-care domain in the UK itself varies broadly in-line with the national boundaries of the constituent states (England, Scotland, Wales and Northern Ireland). Technologies must address these political boundaries and the impact these boundaries have in terms of for example, information governance, policies, and of course large-scale heterogeneous distribution of the data sets themselves. This paper outlines the methodology in implementing the framework between three specific data sources that serve as useful case studies: Scottish data from the Scottish Care Information (SCI) Store data repository, data on the General Practice Research Database (GPRD) diabetes trial at Imperial College London, and benign prostate hypoplasia (BPH) data from the University of Nottingham. The design, implementation and wider research issues are discussed along with the technological challenges encountered in the project in the application of Grid technologies

Rethinking De-Perimeterisation: Problem Analysis And Solutions

For businesses, the traditional security approach is the hard-shell model: an organisation secures all its assets using a fixed security border, trusting the inside, and distrusting the outside. However, as technologies and business processes change, this model looses its attractiveness. In a networked world, “inside” and “outside” can no longer be clearly distinguished. The Jericho Forum - an industry consortium part of the Open Group – coined this process deperimeterisation and suggested an approach aimed at securing data rather than complete systems and infrastructures. We do not question the reality of de-perimeterisation; however, we believe that the existing analysis of the exact problem, as well as the usefulness of the proposed solutions have fallen short: first, there is no linear process of blurring boundaries, in which security mechanisms are placed at lower and lower levels, until they only surround data. To the contrary, we experience a cyclic process of connecting and disconnecting of systems. As conditions change, the basic trade-off between accountability and business opportunities is made (and should be made) every time again. Apart from that, data level security has several limitations to start with, and there is a big potential for solving security problems differently: by rearranging the responsibilities between businesses and individuals. The results of this analysis can be useful for security professionals who need to trade off different security mechanisms for their organisations and their information systems

Development of grid frameworks for clinical trials and epidemiological studies

E-Health initiatives such as electronic clinical trials and epidemiological studies require access to and usage of a range of both clinical and other data sets. Such data sets are typically only available over many heterogeneous domains where a plethora of often legacy based or in-house/bespoke IT solutions exist. Considerable efforts and investments are being made across the UK to upgrade the IT infrastructures across the National Health Service (NHS) such as the National Program for IT in the NHS (NPFIT) [1]. However, it is the case that currently independent and largely non-interoperable IT solutions exist across hospitals, trusts, disease registries and GP practices – this includes security as well as more general compute and data infrastructures. Grid technology allows issues of distribution and heterogeneity to be overcome, however the clinical trials domain places special demands on security and data which hitherto the Grid community have not satisfactorily addressed. These challenges are often common across many studies and trials hence the development of a re-usable framework for creation and subsequent management of such infrastructures is highly desirable. In this paper we present the challenges in developing such a framework and outline initial scenarios and prototypes developed within the MRC funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project [2]

Grid Databases for Shared Image Analysis in the MammoGrid Project

The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing front-end workstation and b) the ability to service real-world clinician queries across a distributed and federated database. The MammoGrid project will prove the viability of the Grid by harnessing its power to enable radiologists from geographically dispersed hospitals to share standardized mammograms, to compare diagnoses (with and without computer aided detection of tumours) and to perform sophisticated epidemiological studies across national boundaries. This paper outlines the approach taken in MammoGrid to seamlessly connect radiologist workstations across a Grid using an "information infrastructure" and a DICOM-compliant object model residing in multiple distributed data stores in Italy and the UKComment: 10 pages, 5 figure