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Large scale text mining for deriving useful insights: A case study focused on microbiome.

Syed Ashif Jardary Al Ahmed1, Nishad Bapatdhar2, Bipin Pradeep Kumar2

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|September 19, 2022
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Large-scale text mining of microbiome research reveals geographical trends and complex relationships between diseases, food, and the microbiome. This approach uncovers novel insights from scientific literature.

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Area of Science:

  • Biomedical Informatics
  • Microbiome Research
  • Computational Biology

Background:

  • Scientific literature is a crucial source of knowledge for biomedical research, including mechanistic pathways and molecular databases.
  • Text mining offers automated tools to extract and utilize the vast information contained within scientific texts.
  • The microbiome field is rapidly growing, necessitating advanced methods for knowledge discovery.

Purpose of the Study:

  • To demonstrate the potential of large-scale text mining for generating novel insights in the microbiome field.
  • To analyze geographical research distributions and economic drivers within microbiome studies.
  • To construct semantic relationship networks to understand connections between diseases, microbiome, and food.

Main Methods:

  • Collected all microbiome-related abstracts from PubMed.
  • Utilized the Taxila text mining and intelligence platform for analysis.
  • Extracted geographical mentions to map research locations.
  • Constructed semantic relationship networks between key concepts (diseases, microbiome, food).

Main Results:

  • Identified geographical patterns and economic influences in microbiome research.
  • Revealed complex interrelationships between diseases, the microbiome, and dietary factors.
  • Demonstrated the ability to derive insights without pre-existing encoded knowledge.

Conclusions:

  • Large-scale text mining, using platforms like Taxila, is effective for uncovering hidden patterns in biomedical literature.
  • Geographical and semantic network analyses provide valuable perspectives on the microbiome research landscape.
  • This methodology facilitates the discovery of novel associations and research directions in the microbiome field.