Detalle de: Extracting information automatically from biological literature

In the past few decades, biologists have generated alarge amount of data that has been publishedmainly in biological journals. It is now importantto be able to recover as much as possible of thisinformation as it constitutes a precious source ofadditional information for helping to understandthe new genomics and proteomics data. More than10 million abstracts of such papers are containedin the Medline collection and are available onthe World Wide Web Via PubMed [10], and thiscollection will expand considerably once journalsbecome freely available on the Web (PubMedCentral [15], E-BioSci [7]).In parallel with these plain text informationsources, basic molecular biology data has beenstored in various semi-structured repositories, suchas protein and gene sequence databases, and morerecently in databases of protein structures, proteininteractions, transcription factors, point mutations,metabolic pathways and many others.There is a commonly-recognized need for linkingand complementing the information contained inthese databases with the information stored in theliterature, a task that right now requires detailedwork by scientists and in some cases database users.