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Text Mining for Building Biomedical Networks Using Cancer as a Case Study.

Sofia I R Conceição1, Francisco M Couto1

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

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Automated text mining tools extract biological interactions from scientific literature to build more accurate biological networks. This review explores deep learning methods for relation extraction, particularly for cancer research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Biological network assembly requires reliable interaction data for accurate system representation.
  • High-throughput assays and scientific literature are primary data sources, but literature volume poses curation challenges.
  • Manual curation is insufficient for tracking discoveries in the rapidly expanding scientific literature.

Purpose of the Study:

  • To review automatic information extraction approaches for enhancing biological networks.
  • To highlight the role of text mining and relation extraction in knowledge discovery.
  • To focus on state-of-the-art deep learning methods using cancer disease as a case study.

Main Methods:

  • Exploration of text mining techniques for knowledge extraction from biomedical documents.
  • Focus on relation extraction methods to identify entity interactions.
  • Review of deep learning approaches for automatic information extraction.

Main Results:

  • Text mining offers an efficient solution for identifying biological interactions from literature.
  • Automated tools can lead to more reliable and personalized biological networks.
  • Deep learning methods show promise for advancing relation extraction in biomedical text.

Conclusions:

  • Automated information extraction is crucial for overcoming the challenges of literature-based biological network construction.
  • Advanced text mining, especially deep learning, can significantly improve the accuracy and scope of biological networks.
  • This approach is particularly valuable for specific research areas like cancer biology.