ABSTRACT
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
9.1 Introduction The burgeoning amount of biological information (e.g., the published literature,
genome and proteome projects) is confronting researchers with challenges in dealing with this large volume of complicated data. With the advance of technologies in both the biological and computer sciences, universities and research centers are producing a huge amount of experimental data, diverse new discoveries, and related publications. For instance, the BioNetbook (BioNetbook) has recognized and collected biological information databases around the world, now totaling 1750 and growing. In addition, they have recognized 8048 relevant Web pages including bibliographical materials, analysis tools, software, and courses in biological research. In their 2004 annual database issue, Nucleic Acids Research has recognized biological information storage sites around the world, classified them into 11 categories of biological information, and listed 548 Web sites under operation (Galperin, 2004). These databases, dispersed in different locations, serve important roles in modern research including the storage of experimental data, the maintenance of information, and the integration of diverse data sources. Researchers often review multiple publications and other databases to arrive at a comprehensive understanding and generate or validate their hypotheses. In this model, biological phenomena may be viewed as being composed of a number of subdisciplines (e.g., structural biology, genomics, proteomics, and biochemistry). However, to obtain a coherent picture of biological phenomena at the molecular, cellular, and organism levels, one must look at both the attributes and the relationships among them. To do that currently requires finding which databases contain the relevant information and then searching through the databases one by one.
