iHOP

Implementing the iHOP concept for navigation of biomedical literature. Motivation: The World Wide Web has profoundly changed the way in which we access information. Searching the internet is easy and fast, but more importantly, the interconnection of related contents makes it intuitive and closer to the associative organization of human memory. However, the information retrieval tools currently available to researchers in biology and medicine lag far behind the possibilities that the layman has come to expect from the internet. Results: By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource. iHOP (Information Hyperlinked over Proteins) is an online service that provides this gene-guided network as a natural way of accessing millions of PubMed abstracts and brings all the advantages of the internet to scientific literature research. Navigating across interrelated sentences within this network is closer to human intuition than the use of conventional keyword searches and allows for stepwise and controlled acquisition of information. Moreover, this literature network can be superimposed upon experimental interaction data to facilitate the simultaneous analysis of novel and existing knowledge. The network presented in iHOP currently contains5 million sentences and 40 000 genes from Homo sapiens , Mus musculus , Drosophila melanogaster , Caenorhabditis elegans , Danio rerio , Arabidopsis thaliana , Saccharomyces cerevisiae and Escherichia coli . Availability: iHOP is freely accessible at http://www.pdg.cnb.uam.es/UniPub/iHOP/


References in zbMATH (referenced in 4 articles )

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  1. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  2. Fuentes-Lorenzo, Damaris; Morato, Jorge; Gómez, Juan Miguel: Knowledge management in biomedical libraries: a semantic web approach (2009) ioport
  3. Wiegers, Thomas C.; Davis, Allan Peter; Cohen, K. Bretonnel; Hirschman, Lynette; Mattingly, Carolyn J.: Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD) (2009) ioport
  4. Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun’ichi: Corpus annotation for mining biomedical events from literature (2008) ioport