Amino Acids

, Volume 44, Issue 3, pp 887–901

An empirical study on the matrix-based protein representations and their combination with sequence-based approaches

Original Article

DOI: 10.1007/s00726-012-1416-6

Cite this article as:
Nanni, L., Lumini, A. & Brahnam, S. Amino Acids (2013) 44: 887. doi:10.1007/s00726-012-1416-6
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Abstract

Many domains have a stake in the development of reliable systems for automatic protein classification. Of particular interest in recent studies of automatic protein classification is the exploration of new methods for extracting features from a protein that enhance classification for specific problems. These methods have proven very useful in one or two domains, but they have failed to generalize well across several domains (i.e. classification problems). In this paper, we evaluate several feature extraction approaches for representing proteins with the aim of sequence-based protein classification. Several protein representations are evaluated, those starting from: the position specific scoring matrix (PSSM) of the proteins; the amino-acid sequence; a matrix representation of the protein, of dimension (length of the protein) ×20, obtained using the substitution matrices for representing each amino-acid as a vector. A valuable result is that a texture descriptor can be extracted from the PSSM protein representation which improves the performance of standard descriptors based on the PSSM representation. Experimentally, we develop our systems by comparing several protein descriptors on nine different datasets. Each descriptor is used to train a support vector machine (SVM) or an ensemble of SVM. Although different stand-alone descriptors work well on some datasets (but not on others), we have discovered that fusion among classifiers trained using different descriptors obtains a good performance across all the tested datasets. Matlab code/Datasets used in the proposed paper are available at http://www.bias.csr.unibo.it\nanni\PSSM.rar.

Keywords

Proteins classification Machine learning Ensemble of classifiers Support vector machines 

Copyright information

© Springer-Verlag Wien 2012

Authors and Affiliations

  • Loris Nanni
    • 1
  • Alessandra Lumini
    • 2
  • Sheryl Brahnam
    • 3
  1. 1.DEIUniversity of PaduaPaduaItaly
  2. 2.DEISUniversità di BolognaCesenaItaly
  3. 3.Computer Information SystemsMissouri State UniversitySpringfieldUSA

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