Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 236717, 17 pages
Research Article

An Empirical Study of Different Approaches for Protein Classification

1Dipartimento di Ingegneria dell’Informazione, Via Gradenigo 6/A, 35131 Padova, Italy
2DISI, Università di Bologna, Via Venezia 52, 47521 Cesena, Italy
3Computer Information Systems, Missouri State University, 901 South National, Springfield, MO 65804, USA

Received 24 March 2014; Revised 5 May 2014; Accepted 7 May 2014; Published 15 June 2014

Academic Editor: Wei Chen

Copyright © 2014 Loris Nanni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art.