A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is
In recent years, a number of machine learning approaches to literature-based gene function annotation have been proposed. However, due to issues such as lack of labeled data, class imbalance and computational cost, they have usually been unable to surpass simpler approaches based on stringmatching. In this paper, we propose a principled machine learning approach based on kernel classifiers. We show that kernels can address the task's inherent data scarcity by embedding additional knowledge anddoi:10.1145/2009916.2010080 dblp:conf/sigir/BlondelSU11 fatcat:rciumsw23fd5rauyoarkh7moai