Jason Weston, Dengyong Zhou, André Elisseeff, William Noble, Christina Leslie
A key issue in supervised protein classiﬁcation is the representation of in- put sequences of amino acids. Recent work using string kernels for pro- tein data has achieved state-of-the-art classiﬁcation performance. How- ever, such representations are based only on labeled data — examples with known 3D structures, organized into structural classes — while in practice, unlabeled data is far more plentiful. In this work, we de- velop simple and scalable cluster kernel techniques for incorporating un- labeled data into the representation of protein sequences. We show that our methods greatly improve the classiﬁcation performance of string ker- nels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods while achieving far greater computational efﬁciency.