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A Supervised Machine Learning Approach of Extracting and Ranking Published Papers Describing Coexpression Relationships among Genes

  • Richa TiwariEmail author
  • Chengcui Zhang
  • Thamar Solorio
  • Wei-Bang Chen
Chapter
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Abstract

In this chapter, we describe a framework to extract information about coexpression relationships among genes from published literature using a supervised machine learning approach, and later rank those papers to provide users with a complete specialized information retrieval system. We use Dynamic Conditional Random Fields (DCRFs), for training our classification model. Our approach is based on semantic analysis of text to classify the predicates describing coexpression rather than detecting the presence of keywords. Our framework outperformed the baseline by almost 52%, with DCRFs showing superior performance to Bayes Net, SVM, and Naïve Bayes classification algorithm. In our second experiment, the comparison of our ranked results to that of PubMed and Google demonstrates that our proposed model performs better than both in distinguishing a positive paper from a negative paper. In conclusion, this chapter describes a specialized classification and ranking framework that can retrieve articles that discuss coexpression among genes.

Keywords

Co-expression Relationships Dynamic Conditional Random Fields (DCRFs) DCRFs Model Chunk Tags Biomedical Entities (BME) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Vienna 2012

Authors and Affiliations

  • Richa Tiwari
    • 1
    Email author
  • Chengcui Zhang
    • 1
  • Thamar Solorio
    • 1
  • Wei-Bang Chen
    • 1
  1. 1.Department of Computer and Information SciencesThe University of Alabama at BirminghamBirminghamUSA

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