On the Role of Local Matching for Efficient Semi-supervised Protein Sequence Classification
Recent studies in protein sequence analysis have leveraged the power of unlabeled data. For example, the profile and mismatch neighborhood kernels have shown significant improvements over classifiers estimated under the fully supervised setting. In this study, we present a principled and biologically motivated framework that more effectively exploits the unlabeled data by only utilizing regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias kernel estimations that rely on unlabeled data, we also propose a method to remove this bias and improve performance of resulting classifiers.Combined with a computationally efficient sparse family of string kernels, our proposed framework achieves state-of-the-art accuracy in semi-supervised protein remote homology detection on three large unlabeled databases.