Table I.

Some examples of the online text-mining tools for biologists

ProgramFeaturesURLReference
PubGeneDesigned to identify relationships between genes based on their co-occurrence in the abstracts of scientific papershttp://www.pubgene.org/Jenssen et al. (2001)
MedMinerFilters extract and organize relevant sentences in the literature based on the query givenhttp://discover.nci.nih.gov/textmining/main.jspTanabe et al. (1999)
XploreMedAllows exploration of a set of abstracts derived from a MEDLINE searchhttp://www.bork.embl-heidelberg.de/xplormed/Perez-Iratxeta et al. (2003)
PubMatrixCompares a list of terms against another list of terms in PubMedhttp://pubmatrix.grc.nia.nih.gov/Becker et al. (2003)
AbXtractExtracts domain-specific information from the analysis of abstracts related to set of protein familieshttp://columba.ebi.ac.uk:8765/andrade/abxAndrade and Valencia (1998)
VxInsightA general tool for revealing the implicit structure of the data in large databaseshttp://www.cs.sandia.gov/projects/VxInsight.htmlKim et al. (2001)
SUISEKIExtracts protein-protein interactions from large collections of scientific texthttp://www.pdg.cnb.uam.es/suiseki/Blaschke and Valencia (2001)
GISBiomedical text-mining system focused on gene-related informationhttp://iir.csie.ncku.edu.tw/∼yuhc/gis/Chiang et al. (2004)
PreBINDLocates biomolecular interaction information in the scientific literaturehttp://www.blueprint.org/products/prebind/Donaldson et al. (2003)
Genes2 DiseasesAnalyses relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databaseshttp://www.bork.embl-heidelberg.de/g2d/Perez-Iratxeta et al. (2002)
HAPILinks set of genes in the published literature by way of keyword hierarchieshttp://array.ucsd.edu/hapiMasys et al. (2001)
TextPressoAn information retrieval and extraction system for biological literature (Caenorhabditis elegans version)http://www.textpresso.org/Muller et al. (2004)
Dragon TF Association MinerFinds association between transcription factors, GO terms, and diseases. Has a module to filter out irrelevant documentshttp://research.i2r.a-star.edu.sg/DRAGON/TFAM_v2/Pan et al. (2004)