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Machine Learning for Biomedical Literature Triage

Author

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  • Hayda Almeida
  • Marie-Jean Meurs
  • Leila Kosseim
  • Greg Butler
  • Adrian Tsang

Abstract

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.

Suggested Citation

  • Hayda Almeida & Marie-Jean Meurs & Leila Kosseim & Greg Butler & Adrian Tsang, 2014. "Machine Learning for Biomedical Literature Triage," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0115892
    DOI: 10.1371/journal.pone.0115892
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    References listed on IDEAS

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    1. Changqin Quan & Meng Wang & Fuji Ren, 2014. "An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-8, July.
    2. Minchao Wang & Wu Zhang & Wang Ding & Dongbo Dai & Huiran Zhang & Hao Xie & Luonan Chen & Yike Guo & Jiang Xie, 2014. "Parallel Clustering Algorithm for Large-Scale Biological Data Sets," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    3. Doug Howe & Maria Costanzo & Petra Fey & Takashi Gojobori & Linda Hannick & Winston Hide & David P. Hill & Renate Kania & Mary Schaeffer & Susan St Pierre & Simon Twigger & Owen White & Seung Yon Rhee, 2008. "The future of biocuration," Nature, Nature, vol. 455(7209), pages 47-50, September.
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    Cited by:

    1. Michelle Viscaino & Juan C Maass & Paul H Delano & Mariela Torrente & Carlos Stott & Fernando Auat Cheein, 2020. "Computer-aided diagnosis of external and middle ear conditions: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-18, March.
    2. Kyubum Lee & Maria Livia Famiglietti & Aoife McMahon & Chih-Hsuan Wei & Jacqueline Ann Langdon MacArthur & Sylvain Poux & Lionel Breuza & Alan Bridge & Fiona Cunningham & Ioannis Xenarios & Zhiyong Lu, 2018. "Scaling up data curation using deep learning: An application to literature triage in genomic variation resources," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.

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