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Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical

Eduardo Rosado1, Miguel Garcia-Remesal1, Sergio Paraiso-Medina1

  • 1Biomedical Informatics Group, School of Computer Science, Universidad Politecnica de Madrid, Madrid, Spain.

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Summary

The Biomedical Database Inventory (BiDI) system automatically identifies and links to over 10,000 biomedical databases from scientific literature. This tool enhances data discovery and access for researchers, facilitating scientific initiatives.

Keywords:
biomedical knowledgebiomedical databasesdeep learninginternetnatural language processing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Existing biomedical literature repositories lack mechanisms for locating and accessing specific biomedical databases.
  • This limitation hinders efficient data retrieval and utilization in scientific research.

Purpose of the Study:

  • To develop the Biomedical Database Inventory (BiDI), a novel repository for discovering and accessing biomedical databases.
  • BiDI aims to automatically extract database information from scientific literature, providing a centralized index and seamless access.

Main Methods:

  • An ensemble of deep learning and natural language processing models was employed for database mention extraction.
  • A dataset of 1242 annotated articles was used for training, incorporating transfer learning techniques.
  • The models achieved high performance in detecting database publications and extracting associated weblinks.

Main Results:

  • The system achieved an F1 score of 0.929 for database detection and 0.908 for weblink extraction.
  • Over 10,000 unique biomedical databases were identified by applying the model to PubMed and PubMed Central.
  • Use cases demonstrated BiDI's utility for 'omics' and COVID-19 research, highlighting its ability to filter irrelevant links.

Conclusions:

  • BiDI significantly improves access to biomedical resources, supporting data-driven research and scientific endeavors.
  • The repository is openly accessible online and will be continuously updated via an automated text processing pipeline.
  • The methodology is adaptable for creating similar repositories across various scientific domains.