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Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning

Hongyin Zhu1,2, Yi Zeng1,2,3,4, Dongsheng Wang5

  • 1Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

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|May 7, 2020
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Summary

This study introduces SpecExplorer, a novel system for automatically identifying species in neuroscience literature. It enhances knowledge discovery by classifying species and their research relevance, aiding AI and brain research communities.

Keywords:
PubMedbrain sciencecognitive computingcorpus annotationlinked brain datamulti-label classificationneuroscience

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Biology

Background:

  • Neuroscience research generates vast amounts of literature, necessitating efficient methods for knowledge extraction.
  • Linking neuroscience findings across species is crucial for advancing brain research, AI, and neurorobotics.
  • Current methods struggle to automatically identify species and their research focus within scientific abstracts.

Purpose of the Study:

  • To develop an automated system (SpecExplorer) for mining species information from neuroscience literature.
  • To distinguish between species mentioned as subjects versus those in a secondary context.
  • To facilitate knowledge discovery and resource linking across different research communities.

Main Methods:

  • Proposed a sequence-to-sequence classification framework for multi-label species assignment.
  • Introduced Hierarchical Attentive Decoding (HAD) to model document structure and extract relevant information (span of interest).
  • Created and utilized three datasets from PubMed and PMC corpora with mention-based and semantic-based annotations.

Main Results:

  • The SpecExplorer system demonstrated improved performance in species classification tasks.
  • Successfully distinguished between primary and secondary species mentions in the literature.
  • Enabled novel species-based analyses of brain diseases, cognitive functions, and hippocampal proteins.

Conclusions:

  • Automated species identification in neuroscience literature is feasible and valuable.
  • The SpecExplorer project offers a scalable solution for knowledge management in neuroscience.
  • Findings provide new avenues for species-specific research directions in brain science and related fields.