Supporting taxonomic names in cell and molecular biology databases

Groups of organisms require labels or names to refer to them; however, the idea of a single static name index, although tempting for its simplicity, is both impractical and unadvisable as a basis for referring to organisms for which data has been collected and stored for analyses and sharing. The relevant issues are described and some of the challenges facing database researchers are discussed.     

Automated extraction of information in molecular biology

We review data mining techniques in molecular biology, specifically those that extract information from the scientific literature itself. As more of the biological literature is published electronically, there is an opportunity, and even a need, to automatically summarize the literature in a customized way, for example by associating keywords to a topic. These keywords can be extracted from relevant publications. The process of keyword extraction can be automated and optimized to keep literature pointers automatically up-to-date or to filter relevant information from the literature. To illustrate these points, OMIM (Online Mendelian Inheritance in Man), a database of human inherited diseases, was linked to the literature and keywords were derived that covered distinct aspects such as genetic information on the one hand and disease-specific protein and phenotypic information on the other. They were used to extract information that is helpful for keeping entries about disease up-to-date.     

Ontologies for molecular biology and bioinformatics

About five years ago, ontology was almost unknown in bioinformatics, even more so in molecular biology. Nowadays, many bioinformatics articles mention it in connection with text mining, data integration or as a metaphysical cure for problems in standardisation of nomenclature and other applications. This article attempts to give an account of what concept ontologies in the domain of biology and bioinformatics are; what they are not; how they can be constructed; how they can be used; and some fallacies and pitfalls creators and users should be aware of.     

Pathway information for systems biology

Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.