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.
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Tiwari, R., Zhang, C., Solorio, T., Chen, WB. (2012). A Supervised Machine Learning Approach of Extracting and Ranking Published Papers Describing Coexpression Relationships among Genes. In: Özyer, T., Kianmehr, K., Tan, M. (eds) Recent Trends in Information Reuse and Integration. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0738-6_14
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