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Enhancing a taxonomy for medicinal plants by incorporating Information Extraction from Biomedical Literature

About Enhancing a taxonomy for medicinal plants by incorporating Information Extraction from Biomedical Literature

In last few years, the volume of experimental data and publications produced in biomedical domain have scaled up rapidly. This enormous amount of knowledge has been stored in text databases such as MEDLINE, PubMed Central, BioMed Central etc. For example, MEDLINE, one of the largest bibliographic text database of biomedical information science, has more than 27 million research articles ever since its establishment in 1946. While on the one hand biomedical literature provide complete description of novel research, on the other hand its unstructured format makes its management and knowledge discovery process tough for the end user. At this juncture, text mining emerged as a feasible solution for knowledge discovery by using artificial intelligence technology, natural language processing, information mining and machine learning to handle large volume of free text. Specifically, in biomedical field, the term "Biomedica1 Text mining" has gained momentum and actua11y it is an integral part of bioinformatics. In biomedical domain, semantic text mining can be more effective than traditional text mining due to complex semantic relation between domain entities and disparity among domain terminologies. Semantic text mining emphasizes on applying semantic knowledge stored in domain ontology to conventional text mining methods. Ontology has a hierarchically organized set of concepts with all possible relationships mentioned among them. This structure helps to precisely connect the new findings in biomedical literature to the existing knowledge base. Recently Medicinal Plant Domain (MPD) in the biomedical domain has emerged as a research sensation for many researchers owing to the psychological drift of common people from allopathy to naturopathy. The tribes and provincial individuals in India have immense information of therapeutic plants and their utilize in curing different health issues. Parts of the plants such as fruits, bark, leaves, roots and blooms are used for various medicinal preparations. Apart from having a wealthy source of supplements and bioactive properties, treatment using medicative plants hold a solid ground because they are treated to be secure and have hardly any adverse effects. Recent time has also seen a significant use of medicinal plants in allopathy as raw ingredient for some crucial drugs.

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  • Language:
  • English
  • ISBN:
  • 9798224047369
  • Binding:
  • Paperback
  • Published:
  • March 7, 2024
  • Dimensions:
  • 216x279x7 mm.
  • Weight:
  • 308 g.
Delivery: 1-2 weeks
Expected delivery: December 1, 2024

Description of Enhancing a taxonomy for medicinal plants by incorporating Information Extraction from Biomedical Literature

In last few years, the volume of experimental data and publications produced in biomedical domain have scaled up rapidly. This enormous amount of knowledge has been stored in text databases such as MEDLINE, PubMed Central, BioMed Central etc. For example, MEDLINE, one of the largest bibliographic text database of biomedical information science, has more than 27 million research articles ever since its establishment in 1946. While on the one hand biomedical literature provide complete description of novel research, on the other hand its unstructured format makes its management and knowledge discovery process tough for the end user. At this juncture, text mining emerged as a feasible solution for knowledge discovery by using artificial intelligence technology, natural language processing, information mining and machine learning to handle large volume of free text. Specifically, in biomedical field, the term "Biomedica1 Text mining" has gained momentum and actua11y it is an integral part of bioinformatics.

In biomedical domain, semantic text mining can be more effective than traditional text mining due to complex semantic relation between domain entities and disparity among domain terminologies. Semantic text mining emphasizes on applying semantic knowledge stored in domain ontology to conventional text mining methods. Ontology has a hierarchically organized set of concepts with all possible relationships mentioned among them. This structure helps to precisely connect the new findings in biomedical literature to the existing knowledge base.

Recently Medicinal Plant Domain (MPD) in the biomedical domain has emerged as a research sensation for many researchers owing to the psychological drift of common people from allopathy to naturopathy. The tribes and provincial individuals in India have immense information of therapeutic plants and their utilize in curing different health issues. Parts of the plants such as fruits, bark, leaves, roots and blooms are used for various medicinal preparations. Apart from having a wealthy source of supplements and bioactive properties, treatment using medicative plants hold a solid ground because they are treated to be secure and have hardly any adverse effects. Recent time has also seen a significant use of medicinal plants in allopathy as raw ingredient for some crucial drugs.

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