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Identifying genotype-phenotype relationships in biomedical text.

Maryam Khordad1, Robert E Mercer2

  • 1Department of Computer Science, University of Western Ontario, 1151 Richmond Street, London, N6A 5B7, Canada. mkhordad@alumni.uwo.ca.

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|December 8, 2017
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Summary

This study introduces a machine learning approach to automatically identify genotype-phenotype relationships in biomedical literature. A self-training method improved initial results, demonstrating the potential for efficient data curation.

Keywords:
Computational linguisticsGenotype-phenotype relationshipGenotypesPhenotypesSelf-trainingSemi-automatic corpus annotation

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomedical literature contains crucial genotype-phenotype relationship data.
  • Manual curation is challenging due to the vast volume of research.
  • Automated systems are essential for efficient data identification and database updates.

Purpose of the Study:

  • To develop and evaluate a machine learning method for identifying genotype-phenotype relationships in biomedical text.
  • To address the lack of large, human-annotated datasets by employing semi-automatic and self-training approaches.

Main Methods:

  • A machine learning model was trained on a small, semi-automatically annotated dataset.
  • A self-training method was utilized to expand the training set and improve model performance.
  • The model's effectiveness was assessed using an expert-annotated test set.

Main Results:

  • Supervised learning on the initial small training set yielded good performance (Precision: 76.47, Recall: 77.61, F-measure: 77.03).
  • The self-training method further enhanced the model's accuracy (Precision: 77.70, Recall: 77.84, F-measure: 77.77).
  • These results demonstrate the efficacy of the proposed approach for genotype-phenotype relationship extraction.

Conclusions:

  • The developed method is the first specialized system for identifying genotype-phenotype relationships in biomedical literature.
  • The approach achieves promising results even with limited training data.
  • Future work should explore additional linguistic contexts and further enlarge the training dataset for improved accuracy.