Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Correspondence
  • Published:

Mining the literature and large datasets

Nature Biotechnology volume 21, pages 976–977 (2003)Cite this article

To the editor:

In the accelerating quest for disease biomarkers, the use of high-throughput technologies, such as DNA microarrays and proteomics experiments, has produced vast datasets identifying thousands of genes whose expression patterns differ in diseased versus normal samples. Although many of these differences may reach statistical significance, they are not always biologically meaningful. For example, reports of mRNA or protein changes of as little as two-fold are not uncommon, and although some changes of this magnitude turn out to be important, most are attributable to disease-independent differences between the samples. Evidence gleaned from other studies linking genes to the disease is helpful, but with such large datasets, a manual literature review is often not practical. Thus, the power of these emerging technologies—the ability to quickly generate large sets of data—has challenged current means of evaluating and validating these data. One study from 1999, for example, reveals that a researcher would have to scan 130 different journals and read 27 papers per day to follow a single disease, such as breast cancer1.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

References

  1. Baasiri, R.A., Glasser, S.R., Steffen, D.L. & Wheeler, D.A. Oncogene 18, 7958–7965 (1999).

    Article  CAS  Google Scholar 

  2. Hu, Y. et al. J. Proteome Res. 2, 405–412 (2003).

    Article  Google Scholar 

  3. Steffen, D.L., Levine, A.E., Yarus, S., Baasiri, R.A. & Wheeler, D.A. Bioinformatics 16, 639–649 (2000).

    Article  CAS  Google Scholar 

  4. Bairoch, A. & Apweiler, R. Nucleic Acids Res. 28, 45–48 (2000).

    Article  CAS  Google Scholar 

  5. Rebhan, M., Chalifa-Caspi, V., Prilusky, J. & Lancet, D. Trends Genet. 13, 163 (1997).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Proteomics, Harvard Medical School, 250 Longwood Ave., BCMP, Boston, 02115, Massachusetts, USA

    Joshua LaBaer

Authors
  1. Joshua LaBaer
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

LaBaer, J. Mining the literature and large datasets. Nat Biotechnol 21, 976–977 (2003). https://doi.org/10.1038/nbt0903-976b

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt0903-976b

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing