NDLI: On the Role of Local Matching for Efficient Semi-supervised Protein Sequence Classification

Content Provider	IEEE Xplore Digital Library
Author	Kuksa, P. Pai-Hsi Huang Pavlovic, V.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ (Kuksa, P.; Pai-Hsi Huang; Pavlovic, V.)
Abstract	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.
Starting Page	217
Ending Page	222
File Size	200209
Page Count	6
File Format	PDF
ISBN	9780769534527
DOI	10.1109/BIBM.2008.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information resources sequence classification Performance gain inexact matching Computer science Accuracy sparse spatial sample kernels Databases string kernels Biology computing Protein sequence semi-supervised learning Labeling Kernel Bioinformatics protein classification
Content Type	Text
Resource Type	Article

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