Literature DB >> 16101418

Information extraction in the life sciences: perspectives for medicinal chemistry, pharmacology and toxicology.

Marc Zimmermann1, Juliane Fluck, Le Thuy Bui Thi, Corinna Kolárik, Kai Kumpf, Martin Hofmann.   

Abstract

Information extraction approaches have been successfully applied to mine the scientific literature in biology and medicine. So far, the main focus of research and development in this domain was on the recognition and extraction of gene and protein names in the context of molecular biology and genome research and on disease names and other medical terms in the context of clinical research. Similar to biology and medical sciences, medicinal chemistry, pharmacology and toxicology are descriptive sciences. However, information extraction approaches in these disciplines encounter a number of problems that are specific to the fact that these scientific areas are essentially centred at chemical compounds and their structures. In this review, we will give a short overview on general information extraction strategies in the life sciences and we will introduce new approaches to apply information extraction to the domain of pharmacology, medicinal chemistry and toxicology. Finally, we will emphasize on how information extraction approaches will support public and commercial research in medicinal chemistry, pharmacology and toxicology by linking information on chemical structures to biological information.

Mesh:

Year:  2005        PMID: 16101418     DOI: 10.2174/1568026054637692

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  5 in total

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Authors:  Wendy A Warr
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Authors:  Wendy A Warr
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Authors:  Saber A Akhondi; Alexander G Klenner; Christian Tyrchan; Anil K Manchala; Kiran Boppana; Daniel Lowe; Marc Zimmermann; Sarma A R P Jagarlapudi; Roger Sayle; Jan A Kors; Sorel Muresan
Journal:  PLoS One       Date:  2014-09-30       Impact factor: 3.240

4.  Automatic identification of relevant chemical compounds from patents.

Authors:  Saber A Akhondi; Hinnerk Rey; Markus Schwörer; Michael Maier; John Toomey; Heike Nau; Gabriele Ilchmann; Mark Sheehan; Matthias Irmer; Claudia Bobach; Marius Doornenbal; Michelle Gregory; Jan A Kors
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

5.  Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

Authors:  Saber A Akhondi; Ewoud Pons; Zubair Afzal; Herman van Haagen; Benedikt F H Becker; Kristina M Hettne; Erik M van Mulligen; Jan A Kors
Journal:  Database (Oxford)       Date:  2016-05-02       Impact factor: 3.451

  5 in total

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