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CoIN: a network analysis for document triage.

Yi-Yu Hsu1, Hung-Yu Kao

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, R.O.C. (Republic of China).

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|November 13, 2013
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Summary
This summary is machine-generated.

Biocurators face exponentially increasing workloads due to more medical articles. The Co-occurrence Interaction Nexus (CoIN) system prioritizes articles for curation, improving efficiency and reducing manual review.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Bioinformatics

Background:

  • The exponential growth of medical literature, particularly in PubMed, has significantly increased the workload for biocurators.
  • Efficiently identifying high-priority articles for curation is crucial for managing this information overload.

Purpose of the Study:

  • To develop an automated system for prioritizing articles for biocurators.
  • To reduce the manual workload and improve the efficiency of the curation process.

Main Methods:

  • Proposed the Co-occurrence Interaction Nexus (CoIN) system for learning and exploring relationships within articles.
  • Constructed a co-occurrence analysis system applicable to PubMed articles for gene, chemical, and disease queries.
  • Utilized co-occurrence features and network centralities to assess article influence and curatability.

Main Results:

  • The network-based approach effectively classified curatable and non-curatable articles.
  • CoIN enabled biocurators to survey ranked lists for specific queries, avoiding irrelevant information.
  • Achieved a 0.778 mean average precision in the BioCreative 2012 triage task, securing second place.

Conclusions:

  • The CoIN system offers an effective method for prioritizing articles for biocurators.
  • This approach helps manage the increasing volume of biomedical literature and streamlines the curation process.