Literature DB >> 27179985

Text mining patents for biomedical knowledge.

Raul Rodriguez-Esteban1, Markus Bundschus2.   

Abstract

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2016        PMID: 27179985     DOI: 10.1016/j.drudis.2016.05.002

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  5 in total

1.  A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

Authors:  David Westergaard; Hans-Henrik Stærfeldt; Christian Tønsberg; Lars Juhl Jensen; Søren Brunak
Journal:  PLoS Comput Biol       Date:  2018-02-15       Impact factor: 4.475

2.  A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.

Authors:  Sharanya Manoharan; Oviya Ramalakshmi Iyyappan
Journal:  Methods Mol Biol       Date:  2022

3.  Assessment of the significance of patent-derived information for the early identification of compound-target interaction hypotheses.

Authors:  Stefan Senger
Journal:  J Cheminform       Date:  2017-04-21       Impact factor: 5.514

4.  Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017.

Authors:  Ming Huang; Maryam Zolnoori; Joyce E Balls-Berry; Tabetha A Brockman; Christi A Patten; Lixia Yao
Journal:  J Med Internet Res       Date:  2019-04-30       Impact factor: 5.428

5.  Pandemics, epidemics, viruses, plagues, and disease: Comparative frequency analysis of a cultural pathology reflected in science fiction magazines from 1926 to 2015.

Authors:  Christopher B Menadue
Journal:  Soc Sci Humanit Open       Date:  2020-09-09
  5 in total

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