Protein-protein interactions classification from text via local learning with class priors

He, Yulan and Chenghua, Lin (2009). Protein-protein interactions classification from text via local learning with class priors. In: 14th International Conference on Applications of Natural Language to Information Systems, 23-26 Jun 2009, Saarbrücken, Germany, pp. 182–191.

DOI: https://doi.org/10.1007/978-3-642-12550-8_15

URL: http://www.springerlink.com/content/978-3-642-1254...

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 semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.

Viewing alternatives

Look up in Google Scholar

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

CORE (COnnecting REpositories)

Recommendations