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Large language model based framework for automated extraction of genetic interactions from unstructured data.

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Summary

We developed GIX, an automated framework for gene interaction extraction from literature. GIX matches manual curation accuracy, improving biological discovery and research efficiency.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Automated extraction of biological interactions from literature is crucial for understanding complex biological systems and drug development.
  • Manual curation is the gold standard but is challenged by the increasing volume and complexity of scientific literature.
  • Existing text mining tools often struggle with identifying relevant literature and accurately inferring relationships from complex sentence structures.

Purpose of the Study:

  • To develop an automated and robust framework, GIX (Gene Interaction Extraction), for extracting biological interactions from scientific literature.
  • To overcome the limitations of manual curation and existing automated methods in terms of speed, accuracy, and handling complex text.
  • To provide a reliable and efficient tool for gene/protein interaction extraction that can augment existing biological datasets.

Main Methods:

  • Utilized pre-trained Large Language Models fine-tuned on gene/protein interaction corpora (LLL, RegulonDB).
  • Developed GIX to identify relevant publications using minimal keywords and optimize sentence selection for reduced computational load.
  • Implemented a Stage-2 relation extraction method with a confidence factor for assessing the reliability of extracted interactions.

Main Results:

  • GIX achieved performance comparable to manual curation on benchmark protein/gene interaction datasets, surpassing state-of-the-art approaches via 10-fold cross-validation.
  • Demonstrated enhanced robustness in automated relation extraction.
  • Showcased GIX's ability to augment datasets with new biological terms and processes and its applicability in inferring E. coli gene circuits.

Conclusions:

  • GIX offers a fully automated, robust, and accurate solution for gene interaction extraction, matching manual curation standards.
  • The framework effectively handles the challenges of large-scale literature analysis, improving efficiency in biological research.
  • GIX has the potential to accelerate the discovery of biological relationships and support systems biology research.