Special issue on data management, analysis, and mining for the life sciences

Abstract

During the last decade, biologists have experienced a fundamental revolution from traditional R&D involving the study of single genes and isolated cellular mechanisms to e-biology that addresses whole cell physiology and complex biological systems. This has been accompanied by an explosion in the number and size of public data resources, and a rapid growth in the variety and volume of laboratory data from microarrays measuring transcription levels simultaneously for hundreds of genes and to protein interaction screens measuring multiple components of protein complexes. Life science data is complex and data sets have complex inter-relationships. The data is often incomplete, uncertain, and can vary significantly across biological replicates. Data can evolve more quickly than the technologies developed to interpret the data. Thus, life sciences data is not well suited for current data models and query paradigms. The challenge for database researchers is developing appropriate technology to manage life sciences data, to make it accessible in an efficient way to scientists, and to provide the appropriate data models and data analysis tools. The last few years has seen emerging activity towards addressing this challenge in the database, data mining, and information retrieval communities. This special issue was organized to feature papers that reflect synergies between computational advances in data management and manipulation and the fields of molecular biology, cell physiology and systems biology. The call for Papers resulted in over 20 submissions in October 2004. Each paper was reviewed by two or three experts. As a result of the first round of review, we accepted