Extracting information automatically from biological literature

In the past few decades, biologists have generated a large amount of data that has been published mainly in biological journals. It is now important to be able to recover as much as possible of this information as it constitutes a precious source of additional information for helping to understand the new genomics and proteomics data. More than 10 million abstracts of such papers are contained in the Medline collection and are available on the World Wide Web Via PubMed [10], and this collection will expand considerably once journals become freely available on the Web (PubMed Central [15], E-BioSci [7]). In parallel with these plain text information sources, basic molecular biology data has been stored in various semi-structured repositories, such as protein and gene sequence databases, and more recently in databases of protein structures, protein interactions, transcription factors, point mutations, metabolic pathways and many others. There is a commonly-recognized need for linking and complementing the information contained in these databases with the information stored in the literature, a task that right now requires detailed work by scientists and in some cases database users.