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nies engaged in biotech research and development have database system ca pacities in excess of 200 terabytes. Such capacity requires refrigerator-sized storage systems and the computational power to search and manipulate data in near real time. Database technology is most valuable to a biotech laboratory when it enables the integration of research, development, and clinical activity. One form of research is data mining, which is the process of extracting mean ingful relationships from usually very large quantities of seemingly unre lated data. Specialized data-mining tools allow biotech researchers to perform complex analyses and predictions on data. A prerequisite to data mining is the availability of a controlled vocabulary that provides a single term for a given concept. A popular controlled vocabulary is the Medical Subject Heading (MeSH), maintained by the U.S. National Library of Medicine, and used with the U.S. government-sponsored PubMed biomed ical literature database.
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The ideal pharmacogenomic laboratory information technology infrastruc ture in Figure 5.1 only hints at the challenges associated with the knowl edge management process described in 4. Every IT installation has issues of reliability, scalability, growth, and performance that limit the knowledge management process. For example, highly reliable installations tend to be based on long-established practices, hardware, and software. However, these older or legacy systems tend to lack the performance of the newest systems. Similarly, systems that are highly reliable in small installa tions may not scale readily without major revision in the underlying infra structure. Furthermore, there are often challenges related to integration, the need for additional IT capacity, from processing power and data stor age to connectivity, and a need for applications and architectures opti mized for biotech work. Implementing an integrated IT infrastructure capable of supporting a pharmacogenic laboratory requires more than a collection of servers, large hard drives, a network, and the associated cables and electronics. The physical ergonomic and virtual interface environments must complement each other, and the degree to which these and other components are inte grated and available as a secure, collaborative environment de ne the us ability and effectiveness of the system. In addition to hardware and software tools, technical personnel must be available to operate, maintain, and upgrade the system on a regular basis. Few IT systems in biotech offer seamless integration of data because of multiple standards or because of holes in the knowledge management process. For example, many databases used in pharmacogenomic research
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and development use proprietary formats. Even when data are in a recog nizable format, there must be interfaces between applications and data bases to provide the logical connectivity for communication through the network infrastructure. In addition, few laboratories or medical facilities provide the degree of connectivity suggested by the lab in Figure 5.1. For example, less than 5 percent of hospitals in the United States have a full electronic medical record (EMR) and many of them offer only summary information. Fur thermore, these systems typically require researchers to learn several ar cane languages and procedures to access all data that may be relevant to a given patient research project,, and the results of clinical studies may be maintained in separate databases that aren t connected to the main hospi tal network. There is often little extra storage or computational capacity in biotech IT infrastructures, owing to the kinds of problems addressed by biotech re searchers and the general nature of computing. As the study of the struc ture and function of proteins (proteomics) eclipses that of relatively simple genetic sequences (genomics), the computational complexity and data in volved in the computations will increase by several orders of magnitude. For example, while there are perhaps only about 35,000 genes in the hu man genome, these genes code for more than a million proteins. Under standing the normal function of these proteins and their roles in disease will require supercomputer power, high-bandwidth network and Internet connectivity, and a seamless, secure, collaborative environment. Providing security isn t simply a prudent step, but a legal requirement, as with the Health Insurance Portability and Accountability Act (HIPAA), which became effective in April of 2003. The Act sets minimum standards of security, access, and control for all health-care organizations, including biotech laboratories that use clinical data. At the hardware and software level, security can be provided by applications and hardware architectures that rely on username and password software protection schemes, secure ID cards, and biometrics such as voice, ngerprint, and retinal recognition. In addition, process issues, such as resource management, knowledge man agement policies and guidelines, and process de nitions are just as impor tant in de ning the security of a collaborative environment. Even as the biotech-speci c supercomputer projects become commer cially viable and generally available, supportive political and legal envi ronments will continue to be necessary for true collaboration. Projects such as IBM s Blue Gene, and emergent technologies such as Web services and grid computing architectures will soon become commercially viable. These technologies may necessitate new legislation regarding the sharing of sensitive information such as biological data that may be used for weapons development.
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