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

Future opportunities and trends for e-infrastructures and life sciences: Going beyond the grid to enable life science data analysis

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community

The Changing Scientific and Technological Basis of the CBW Proliferation Problem

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Life Science Research and Drug Discovery at the Turn of the 21st Century: The Experience of SwissBioGrid

Background It is often said that the life sciences are transforming into an information science. As laboratory experiments are starting to yield ever increasing amounts of data and the capacity to deal with those data is catching up, an increasing share of scientific activity is seen to be taking place outside the laboratories, sifting through the data and modelling “in silico” the processes observed “in vitro.” The transformation of the life sciences and similar developments in other disciplines have inspired a variety of initiatives around the world to create technical infrastructure to support the new scientific practices that are emerging. The e-Science programme in the United Kingdom and the NSF Office for Cyberinfrastructure are examples of these. In Switzerland there have been no such national initiatives. Yet, this has not prevented scientists from exploring the development of similar types of computing infrastructures. In 2004, a group of researchers in Switzerland established a project, SwissBioGrid, to explore whether Grid computing technologies could be successfully deployed within the life sciences. This paper presents their experiences as a case study of how the life sciences are currently operating as an information science and presents the lessons learned about how existing institutional and technical arrangements facilitate or impede this operation. Results SwissBioGrid gave rise to two pilot projects: one for proteomics data analysis and the other for high-throughput molecular docking (“virtual screening”) to find new drugs for neglected diseases (specifically, for dengue fever). The proteomics project was an example of a data management problem, applying many different analysis algorithms to Terabyte-sized datasets from mass spectrometry, involving comparisons with many different reference databases; the virtual screening project was more a purely computational problem, modelling the interactions of millions of small molecules with a limited number of protein targets on the coat of the dengue virus. Both present interesting lessons about how scientific practices are changing when they tackle the problems of large-scale data analysis and data management by means of creating a novel technical infrastructure. Conclusions In the experience of SwissBioGrid, data intensive discovery has a lot to gain from close collaboration with industry and harnessing distributed computing power. Yet the diversity in life science research implies only a limited role for generic infrastructure; and the transience of support means that researchers need to integrate their efforts with others if they want to sustain the benefits of their success, which are otherwise lost

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks