Protein-Protein Interactions Classification from Text via Local Learning with Class Priors

  • Yulan He
  • Chenghua Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5723)


Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.


Text classification Protein-protein interactions Semi-supervised learning Local learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yulan He
    • 1
  • Chenghua Lin
    • 1
  1. 1.School of Engineering, Computing and MathematicsUniversity of ExeterExeter

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