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  • Review Article
  • Published:

Literature mining for the biologist: from information retrieval to biological discovery

Nature Reviews Genetics volume 7, pages 119–129 (2006)Cite this article

Key Points

  • Literature-mining tools are becoming essential to researchers because of the growth of the scientific literature and the shift from studying individual genes and proteins to entire systems.

  • Currently, information-retrieval tools such as PubMed are by far the most commonly used literature-mining methods among biologists.

  • Methods for identifying the genes, proteins and other entities that are mentioned in the literature — known as entity recognition — are key components of most complex literature-mining systems.

  • Recently, methods for extracting biomedical facts from text have improved considerably. Such methods will probably soon become mainstream tools for the annotation and analysis of large-scale experimental data sets.

  • By combining facts that have been extracted from several papers, text-mining methods can discover both global trends and generate new hypotheses that are based on the existing literature.

  • To realize the full discovery potential of literature mining, it should be integrated with other data types. Protein networks are well suited for unifying large-scale experimental data with knowledge that has been extracted from the biomedical literature.

  • Data-integration methods have also been developed for ranking candidate genes for inherited diseases and for associating genes with phenotypic characteristics.

  • Bridging the gap between biologists and computational linguists will be crucial to the success of approaches that integrate literature mining with high-throughput experimental data. We hope that this review will inspire more biologists to become actively involved in the development of literature-mining tools.

Abstract

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

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Figure 1: Growth of Medline.
Figure 2: The current state of biomedical literature mining.
Figure 3: A literature-derived network for yeast.
Figure 4: Correlating phenotypes with genotypes.

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Acknowledgements

The authors would like to thank T. Doerks and S. Hooper for help with figures, and other group members of P.B.'s group at the European Molecular Biology Laboratory and I. Rojas's group at EML Research for valuable discussions. J.S. is funded by the Klaus Tschira Foundation. This work was supported by grants from the European Community and the German Ministry for Education and Science through Nationales Genomforschungsnetz (NGFN).

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Authors and Affiliations

  1. European Molecular Biology Laboratory, Heidelberg, D-69117, Germany

    Lars Juhl Jensen & Peer Bork

  2. EML Research gGmbH, Heidelberg, D-69118, Germany

    Jasmin Saric

  3. Max Delbrück Centre for Molecular Medicine, Berlin, D-13092, Germany

    Peer Bork

Authors
  1. Lars Juhl Jensen
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  2. Jasmin Saric
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  3. Peer Bork
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Corresponding authors

Correspondence to Lars Juhl Jensen or Peer Bork.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Related links

Related links

DATABASES

OMIM

Alzheimer disease

Raynaud disease

FURTHER INFORMATION

An extended bibliography of biological literature-mining papers

Arrowsmith

Bitola

Ensembl

G2D

Genia

iHOP

HighWire Press

Medline

MedMiner

NCBI RefSeq

PennBioIE

PubMed

PubMed Central

STRING

Saccharomyces Genome Database

Textpresso

Glossary

Machine learning

The ability of a machine to learn from experience or extract knowledge from examples in a database. Artificial neural networks and support-vector machines are two commonly used types of machine-learning method.

Gene Ontology

A set of controlled vocabularies that are used to describe the molecular functions of a gene product, the biological processes in which it participates and the cellular components in which it can be found.

Syntax

The orderly manner in which words are put together to form phrases and sentences.

Semantics

The meaning that is implied by words and sentences. If an information-extraction method extracts the right facts from a sentence, it has interpreted the semantics correctly.

Anaphoric relationships

Back-references to previously mentioned entities. A protein that is mentioned in an earlier sentence might, for example, be subsequently be referred to as 'it'.

Corpus

A collection of texts. A corpus might consist of either the raw text only (for example, Medline) or be tagged so that, for example, gene and protein names are labelled (for example, GENIA).

Study bias

Study biases arise because some proteins (or other molecules) are more studied than others. For example, if a protein is known to be phosphorylated, it is also more likely to have been studied in other respects, and is therefore more likely to be known to be regulated by expression, for example.

MeSH terms

A controlled vocabulary that is used for annotating Medline abstracts. Several classes of MeSH term exist, the most relevant for literature mining being 'Chemicals and Drugs' (MeSH-D) and 'Diseases' (MeSH-C).

Linkage mapping

A method for localizing genes that is based on the co-inheritance of genetic markers and phenotypes in families over several generations.

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Jensen, L., Saric, J. & Bork, P. Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet 7, 119–129 (2006). https://doi.org/10.1038/nrg1768

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