Application of Semantic Kernels to Literature-Based Gene Function Annotation

  • Mathieu Blondel
  • Kazuhiro Seki
  • Kuniaki Uehara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6926)


In recent years, a number of machine learning approaches to literature-based gene function annotation have been proposed. However, due to issues such as lack of labeled data, class imbalance and computational cost, they have usually been unable to surpass simpler approaches based on string-matching. In this paper, we investigate the use of semantic kernels as a way to address the task’s inherent data scarcity and we propose a simple yet effective solution to deal with class imbalance. From experiments on the TREC Genomics Track data, our approach achieves better F 1-score than two state-of-the-art approaches based on string-matching and cross-species information.


gene annotation text classification kernel methods 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mathieu Blondel
    • 1
  • Kazuhiro Seki
    • 1
  • Kuniaki Uehara
    • 1
  1. 1.Kobe UniversityNadaJapan

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