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Mining the bibliome

The Pharmacogenomics Journal volume 1, pages 88–89 (2001)Cite this article

Consider the natural history of the communication of biological data. In the pre-genomic era, the scientific literature was essentially the only means by which experimental results were conveyed to the community at large. Data laboriously acquired by what we now view as ‘low-throughput’ methods were filtered through the experimentalist’s expository and interpretive medium, ie, the standard journal article. Not only was research hypothesis-driven, but the presentation of its results was as well, in the sense that data were generally offered in the context of some hypothesis, with considerable ‘value-added’ explanation, interpretation, and speculation.

With the advent of genomics, the paradigm shifted, as paradigms are wont to do. Taking advantage of new technologies and economies of scale, researchers accumulated data for its own sake, in the expectation that hypotheses could be tested after the fact using new bioinformatics techniques on extensive databases. The databases, in fact, were expected to spark the hypotheses themselves, in the first instance through their capacity to support novel gene identification. This new mode of research required the application of large-scale database technologies to molecular biology, and for purposes of access, the arrival of the World Wide Web was both timely and felicitous.

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References

  1. Jenssen TK, Laegreid A, Komorowski J, Hovig E . A literature network of human genes for high-throughput analysis ofgene expression Nature Genet 2001 28: 21–28

    CAS  Google Scholar 

  2. Ashburner M et al . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nature Genet 2000 25: 25–29

    Article  CAS  Google Scholar 

  3. Thomas J, Milward D, Ouzounis C, Pulman S, Carroll M . Automatic extraction of protein interactions from scientific abstracts Pacific Symp Biocomput 2000 1999 541–552

  4. Boguski MS . Biosequence exegesis Science 1999 286: 453–455

    Article  CAS  Google Scholar 

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  1. Bioinformatics Division, GlaxoSmithKline Pharmaceuticals, King of Prussia, PA, USA

    D B Searls

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  1. D B Searls
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Correspondence to D B Searls.

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Searls, D. Mining the bibliome. Pharmacogenomics J 1, 88–89 (2001). https://doi.org/10.1038/sj.tpj.6500030

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