Floating search methods in feature selection
Pattern Recognition Letters
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Machine learning multi-classifiers for peptide classification
Neural Computing and Applications
Predictive vaccinology: optimisation of predictions using support vector machine classifiers
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Bio-basis function neural network for prediction of protease cleavage sites in proteins
IEEE Transactions on Neural Networks
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Objective: In this paper we propose a new feature extractor for peptide/protein classification based on the calculation of texture descriptors. Representing a peptide/protein using a matrix descriptor, instead of a vector, allows to deal with the peptide/protein as an image and to use texture descriptors for representation purposes. Methods and materials: A matrix descriptor, which is a squared matrix of the dimension of the peptide/protein, is obtained considering a partial ordering of the amino acids of the peptide/protein according to their value of a given physicochemical property. Each matrix descriptor is considered as a texture image and several texture descriptors are considered to obtain a compact representation which is scale invariant (i.e. independent on the length of the peptide\protein). The texture descriptors tested in this work are: local binary patterns (LBP), discrete cosine transform (DCT) and Daubechies wavelets. Results and conclusion: The experimental section reports several tests, aimed at supporting our ideas, performed on the following datasets: vaccine dataset for the predictions of peptides that bind human leukocyte antigens; human immunodeficiency virus (HIV-1) protease cleavage site prediction dataset and membrane proteins type dataset. The experimental results confirm the usefulness of the novel descriptors: the performance obtained by our system on the three difficult datasets is quite high, indicating that the proposed method is a feasible system for extracting information from peptides and proteins. The performance obtained by each of the three texture descriptors calculated from the matrix-based representation, and coupled to a support vector machine classifier, is lower than the performance obtained by other vector-based descriptors based on physicochemical properties proposed in the literature. Anyway the new descriptors bring different information and our tests show that the texture descriptors and the vector-based descriptors can be combined to improve the overall performance of the system. In particular the proposed approach improves the state-of-the-art results in two out of three tested problems (HIV-1 protease cleavage site prediction dataset and membrane proteins type dataset).