A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets

2007-01-01
In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

Suggestions

A neural network method for direction of arrival estimation with uniform circular dipole array in the presence of mutual coupling
Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Guelbin (2007-06-16)
In recent years application of Neural Network (NN) algorithms in both target tracking problem and DoA estimation have become popular because of the increased computational efficiency This paper presents the implementation of modified neural network algorithm(MN-MUST) to the uniform circular dipole array in the presence of mutual coupling. In smart antenna systems, mutual coupling between elements can significantly degrade the processing algorithms. In this paper mutual coupling affects on MN-MUST has been i...
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
A robust optimization approach for the breast cancer targeted design of PEtOx-b-PLA polymersomes
Oz, Umut Can; Bolat, Zeynep Busra; Ozkose, Umut Ugur; Gulyuz, Sevgi; Kucukturkmen, Berrin; Khalily, Melek Parlak; Özçubukçu, Salih; Yilmaz, Ozgur; Telci, Dilek; ESENDAĞLI, GÜNEŞ; Sahin, Fikrettin; Bozkir, Asuman (2021-04-01)
© 2021 Elsevier B.V.The equipping of nanoparticles with the peptide moiety recognizing a particular receptor, enables cell or tissue-specific targeting, therefore the optimization of the targeted nanoparticles is a key factor in the formulation design process. In this paper, we report the optimization concept of Doxorubicin encapsulating PEtOx-b-PLA polymersome formulation equipped with Peptide18, which is a breast cancer recognizing tumor homing peptide, and the unveiling of the cell-specific delivery pote...
A parallel ant colony optimization algorithm based on crossover operation
Kalınlı, Adem; Sarıkoç, Fatih (Springer, 2018-11-01)
In this work, we introduce a new parallel ant colony optimization algorithm based on an ant metaphor and the crossover operator from genetic algorithms.The performance of the proposed model is evaluated usingwell-known numerical test problems and then it is applied to train recurrent neural networks to identify linear and nonlinear dynamic plants. The simulation results are compared with results using other algorithms.
An Information Theoretic Approach to Classify Cognitive States Using fMRI
Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-11-13)
In this study, an information theoretic approach is proposed to model brain connectivity during a cognitive processing task, measured by functional Magnetic Resonance Imaging (fMRI). For this purpose, a local mesh of varying size is formed around each voxel. The arc weights of each mesh are estimated using a linear regression model by minimizing the squared error. Then, the optimal mesh size for each sample, that represents the information distribution in the brain, is estimated by minimizing various inform...
Citation Formats
H. OĞUL and Ü. E. Mumcuoğlu, “A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets,” BIOSYSTEMS, pp. 75–81, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30345.