Protein Sequence Classification Using Feature Hashing

Cornelia Caragea, Adrian Silvescu, Prasenjit Mitra
2011 2011 IEEE International Conference on Bioinformatics and Biomedicine  
Recent advances in next-generation sequencing technologies have resulted in an exponential increase in the rate at which protein sequence data are being acquired. The k-gram feature representation, commonly used for protein sequence classification, usually results in prohibitively high dimensional input spaces, for large values of k. Applying data mining algorithms to these input spaces may be intractable due to the large number of dimensions. Hence, using dimensionality reduction techniques
more » ... be crucial for the performance and the complexity of the learning algorithms. In this paper, we study the applicability of a recently introduced feature hashing technique to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features, using a hash function, into a lower-dimensional space, i.e., mapping features to hash keys, where multiple features can be mapped (at random) to the same hash key, and "aggregating" their counts. We compare feature hashing with the "bag of kgrams" and feature selection approaches. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks.
doi:10.1109/bibm.2011.91 dblp:conf/bibm/CarageaSM11 fatcat:sjtl2bbt3bf45b6i2mrp3rmsga