High performance computing in biomedical informatics (original) (raw)
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Data mining is being increasingly used in biology. Biologists are adopting prototyping languages, like R and Matlab, to facilitate the application of data mining algorithms to their data. As a result, their scripts are becoming increasingly complex, and also require frequent updates. Application to large datasets becomes impractical and the time-to-paper increases. Furthermore, even if there are various systems that can be used to efficiently process large datasets, for example using Cloud and High Performance Computing, they usually require procedures to be translated into specific languages or adapted to a certain computing platform. Such modifications can speed up the processing but translation is not automatic, especially in complex cases, and can require a large amount of rogramming effort and accurate validation. In this paper, we propose an approach to parallelize data mining procedures in the form of compiled software or R scripts developed by biology communities of practice. Our approach requires minimal alteration of the original code. In many cases, there is no need for code modification. Furthermore, it allows for fast updating when a new version is ready.We clarify the constraints and the benefits of our method and report a practical use case to demonstrate such benefits compared with a standard execution. Our approach relies on a distributed network of web services and ultimately exposes the algorithms as-a-Service, to be invoked by remote thin clients.
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Similar scenarios will occur in other areas: we will see large numbers of radiological images generated in hospitals and immense product and customer databases as the Internet and e-commerce continue to expand. 1 Exploring useful information from such data will require efficient parallel algorithms running on high-performance computing systems with powerful parallel I/O capabilities. Without such systems and techniques, invaluable data and information will remain undiscovered.
Medicine is undergoing a revolution that is even transforming the nature of health care from reactive to proactive. To serve these new and diverse needs, bioinformatics and data mining are teaming up to generate tools and procedures for prediction of disease recurrence and progression, response to treatment, as well as new insights into various oncogenic pathways by taking into account the user needs and their heterogeneity. The p-medicine consortium (http://www.p-medicine.eu) is creating a biomedical platform to facilitate the translation from current practice to a predictive, personalized, preventive, participatory and psycho-cognitive medicine by integrating VPH models, clinical practice, imaging and omics data. In this paper, we present the challenges for data mining based analysis in bio-and medical informatics and our approach towards a data mining environment addressing these requirements in the p-medicine platform.
Scaling up Data Mining Techniques to Large Datasets Using Parallel and Distributed Processing
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Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.