Content-rich biological network constructed by mining PubMed abstracts.

ABSTRACT:

Background

The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulat

SUBMITTER: Chen H 

PROVIDER: S-EPMC528731 | biostudies-literature | 2004 Oct

REPOSITORIES: biostudies-literature