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BACKGROUND Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part.(More)
BACKGROUND Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for(More)
Automatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumption that the most informative terms in top-retrieved documents can be viewed as context of the query and thus can be used for query expansion. One problem with these approaches is that some of the(More)
Protein-protein interactions play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to(More)
Automatic extracting protein–protein interaction information from biomedical literature can help to build protein relation network, predict protein function and design new drugs. This paper presents a protein–protein interaction extraction system BioPPIExtractor for biomedical literature. This system applies Conditional Random Fields model to tag protein(More)
Currently, there is an urgent need to develop a technology for extracting drug information automatically from biomedical texts, and drug name recognition is an essential prerequisite for extracting drug information. This article presents a machine-learning-based approach to recognize drug names in biomedical texts. In this approach, a drug name dictionary(More)
MOTIVATION Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve. RESULTS In this article, we present a syntax(More)