review · Current Pharmaceutical Design · 2007
Bio-Basis Function Neural Networks in Protein Data Mining
Zheng Rong Yang, Rebecca Hamer
Cited by 12Abstract and details only
Abstract
Accurately identifying functional sites in proteins is one of the most important topics in bioinformatics and systems biology. In bioinformatics, identifying protease cleavage sites in protein sequences can aid drug/inhibitor design. In systems biology, post-translational protein-protein interaction activity is one of the major components for analyzing signaling pathway activities. Determining functional sites using laboratory experiments are normally time consuming and expensive. Computer programs have therefore been widely used for this kind of task. Mining protein sequence data using computer programs covers two major issues: 1) discovering how amino acid specificity affects functional sites and 2) discovering what amino acid specificity is. Both need a proper coding mechanism prior to using a proper machine learning algorithm. The development of the bio-basis function neural network (BBFNN) has made a new way for protein sequence data mining. The bio-basis function used in BBFNN is biologically sound in well coding biological information in protein sequences, i.e. well measuring the similarity between protein sequences. BBFNN has therefore been outperforming conventional neural networks in many subjects of protein sequence data mining from protease cleavage site prediction to disordered protein identification. This review focuses on the variants of BBFNN and their applications in mining protein sequence data.
Cite this paper
Yang, Z. R., & Hamer, R. (2007). Bio-Basis Function Neural Networks in Protein Data Mining. Current Pharmaceutical Design, 13(14), 1403–1413. https://doi.org/10.2174/138161207780765927
Yang, Zheng Rong, and Rebecca Hamer. “Bio-Basis Function Neural Networks in Protein Data Mining.” Current Pharmaceutical Design, vol. 13, no. 14, 2007, pp. 1403–13, https://doi.org/10.2174/138161207780765927.
Yang, Zheng Rong, and Rebecca Hamer. 2007. “Bio-Basis Function Neural Networks in Protein Data Mining.” Current Pharmaceutical Design 13 (14): 1403–13. https://doi.org/10.2174/138161207780765927.
Yang, Z.R. and Hamer, R. (2007) ‘Bio-Basis Function Neural Networks in Protein Data Mining’, Current Pharmaceutical Design, 13(14), pp. 1403–1413. Available at: https://doi.org/10.2174/138161207780765927.
@article{yang2007basis,
title = {{Bio-Basis Function Neural Networks in Protein Data Mining}},
author = {Yang, Zheng Rong and Hamer, Rebecca},
journal = {Current Pharmaceutical Design},
volume = {13},
number = {14},
pages = {1403--1413},
year = {2007},
doi = {10.2174/138161207780765927}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
Identification of protein functions using a machine-learning approach based on sequence-derived properties
Bum Ju Lee, Moon Sun Shin, Young Joon Oh et al. · Proteome Science · 2009
Cited by 51Open access
Multiple sequence alignments as tools for protein structure and function prediction
Alfonso Valencia · Comparative and Functional Genomics · 2003
Cited by 7Open access
Functionality of System Components: Conservation of Protein Function in Protein Feature Space
Lars Juhl Jensen, David Wayne Ussery, Søren Brunak · Genome Research · 2003
Cited by 46Open access
Identification of function-associated loop motifs and application to protein function prediction
Jordi Espadaler, Enrique Querol, Francesc Xavier Avilés et al. · Bioinformatics · 2006
Cited by 52
Bio-Basis Function Neural Networks in Protein Data Mining
Zheng Rong Yang, Rebecca Hamer · Current Pharmaceutical Design · 2007
Cited by 13
Protein Meta-Functional Signatures from Combining Sequence, Structure, Evolution, and Amino Acid Property Information
Kai Wang, Jeremy A. Horst, Gong Cheng et al. · PLoS Computational Biology · 2008
Cited by 39Open access