@inbook{932b747ae4034b21b147e04ad86f360d,
title = "Protein-protein interactions classification from text via local learning with class priors",
abstract = "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 semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.",
keywords = "text classification, protein-protein interactions extraction, semi-supervised learning, local learning",
author = "Yulan He and Chenghua Lin",
year = "2009",
doi = "10.1007/978-3-642-12550-8_15",
language = "English",
isbn = "3-642-12549-2",
volume = "5723",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "182--191",
editor = "Helmut Horacek and Elisabeth M{\'e}tais and Rafael Mu{\~n}oz and Magdalena Wolska",
booktitle = "Natural language processing and information systems",
address = "Germany",
note = "14th international conference on applications of natural language to information systems, NLDB 2009 ; Conference date: 24-06-2009 Through 26-06-2009",
}