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{bio,medical} informatics

Wednesday, November 27, 2002

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find related articles. powered by google. The O'Reilly Network Relational Modeling of Biological Data: Trees and Graphs

"While these projects have rich public Web sites for browsing and downloading the data, none of them provide the means to compute on the data, to relate your own data to theirs, or to query across the disparate resources. Fortunately, all of these data sources can be freely obtained by ftp or anonymous cvs for integration into a local, custom-made relational database. But how does one represent hierarchical data in a relational database? And, more importantly, how can one make efficient use of such data?"

"The lesson here is that often the obvious way to model data (such as adjacency lists of tree or graph edges) is not the most efficient data model with which to query or compute. Alternatives such as the nested-set implicit representation of a tree-like hierarchy, or the transitive closure of a graph structure costs very little to create, and provides one-step mechanisms to extract informative subsets of the data. Commercial relational database products that advertise native support for hierarchical data are often simply transparently building and managing these same representations. Now you know how to it yourself."

[ rhetoric ]

Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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