Combining classifiers for improved classification of proteins from sequence or structure.
BACKGROUND
Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage.


RESULTS
In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold.


CONCLUSIONS
In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage. Code and data sets are available at http://noble.gs.washington.edu/proj/sabretooth.
Melvin
Iain
I
NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com
Weston
Jason
J
Leslie
Christina S
CS
Noble
William S
WS
D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism / Q000648:ultrastructure*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D018511:Systems Integration
D011506:Proteins