@inproceedings{d367ce14c8174782a044a89890437db8,
title = "Using maximum entropy model to extract protein-protein interaction information from biomedical literature",
abstract = "Protein-Protein interaction (PPI) information play a vital role in biological research. This work proposes a two-step machine learning based method to extract PPI information from biomedical literature. Both steps use Maximum Entropy (ME) model. The first step is designed to estimate whether a sentence in a literature contains PPI information. The second step is to judge whether each protein pair in a sentence has interaction. Two steps are combined through adding the outputs of the first step to the model of the second step as features. Experiments show the method achieves a total accuracy of 81.9\% in BC-PPI corpus and the outputs of the first step can effectively prompt the performance of the PPI information extraction.",
keywords = "Machine learning, Maximum entropy, Protein-protein interaction, Text mining",
author = "Chengjie Sun and Lei Lin and Xiaolong Wang and Yi Guan",
year = "2007",
doi = "10.1007/978-3-540-74171-8\_72",
language = "英语",
isbn = "9783540741701",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "730--737",
booktitle = "Advanced Intelligent Computing Theories and Applications",
address = "德国",
note = "3rd International Conference on Intelligent Computing, ICIC 2007 ; Conference date: 21-08-2007 Through 24-08-2007",
}