Klasifikasi Fungsi Family Protein Transport Menggunakan Radial Basis Neural Network
DOI:
https://doi.org/10.31154/cogito.v5i2.191.203-214Abstract
Transporter adalah protein transmembran yang penting dalam proses masuk dan keluarnya ion atau molekul sel di seluruh protein membran dan memainkan peran penting dalam mengenali sistem kekebalan tubuh dan transduser energi. Dalam beberapa tahun terakhir, penelitian sebelumnya telah dilakukan untuk menganalisis protein transport, terutama diskriminasi kelas dan familynya dalam memainkan peran penting dalam system control sel, mengangkut air, sinyal kimia dan listrik. Protein transport membrane cenderung membentuk system pompa dan channel span, serta span cell membrane. Oleh karena itu, membedakan kelas dan family transport protein adalah tugas penting dalam ilmu komputasi biologi dan diperlukan bagi para ahli biologi untuk mendapatkan pemahaman yang lebih baik tentang fungsi protein transport. Oleh karena itu, dalam penelitian ini, telah dilakukan pengembangan metode untuk mengidentifikasi fungsi kelas utama dan family protein transport menggunakan radial basis neural network. Peneliti telah mengalanisis karakteristik komposisi asam amino, komposisi residu pair pada protein transport. Metode dalam klasifikasi kelas protein transport untuk mengetahui fungsi protein transport peneliti menggunakan PSSM dengan metode quickRBF classifier memberikan hasil akurasi terbaik dibanding dengan metode yang lain. Hasil akurasi sebesar 84,84% untuk cross validation dan 80,71% untuk independent data, oleh karena itu maka motode yang peneliti usulkan dapat digunakan secara efektif untuk mengidentifikasi dan mendiskriminasi transporter ke dalam kelas protein transport dengan peningkatan 6-10 % dari penelitian yang sejenis.Keywords—transporter, membran, quickRBF, PSSMReferences
M. Pop, and S.L. Salzberg, Bioinformatics challenges of new sequencing technology. Trends in Genetics, 2008. 24(3): p. 142-149.
Martí-Renom, M.A., et al., "Comparative protein structure modeling of genes and genomes," Annual review of biophysics and biomolecular structure, 2000. 29(1): p. 291-325.
M. B. Eisen , et al., "Cluster analysis and display of genome-wide expression patterns," Proceedings of the National Academy of Sciences, 1998. 95(25): p. 14863-14868.
R. Wernersson, and A.G. Pedersen, "RevTrans: multiple alignment of coding DNA from aligned amino acid sequences," Nucleic acids research, 2003. 31(13): p. 3537-3539.
L. Holm, and C. Sander, "Protein structure comparison by alignment of distance matrices" Journal of molecular biology, 1993. 233(1): p. 123-138.
D. T. Jones, "Protein secondary structure prediction based on position-specific scoring matrices," Journal of molecular biology, 1999. 292(2): p. 195-202.
G. A. Sandag, and S. W. Taju. "Bioinformatics Tools for Data Processing and Prediction of
Protein Function". CogITo Smart Journal, 4(2), 2019. 305-315.
D. T. -H. Chang, et al., "Prediction of protein secondary structures with a novel kernel density estimation based classifier," BMC research notes, 2008. 1(1): p. 51.
U. Consortium, Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic acids research, 2011: p. gkr981.
Y.-Y. Ou, QuickRBF: an efficient RBFN package. software available at : http://csie/.org/~ yien/quickrbf/quickstart. php, 2005.
Z. R. Yang, and R. Thomson, "Bio-basis function neural network for prediction of protease cleavage sites in proteins," IEEE Transactions on Neural Networks, 2005. 16(1): p. 263-274.
G.-Z. Zhang, and D.-S. Huang, "Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme," Journal of computer-aided molecular design, 2004. 18(12): p. 797-810.
C.-T. Su, C.-Y. Chen, and Y.-Y. Ou, "Protein disorder prediction by condensed PSSM considering propensity for order or disorder," Bmc Bioinformatics, 2006. 7(1): p. 319.
Y.-Y. Ou, et al., "TMBETADISC-RBF: discrimination of-barrel membrane proteins using RBF networks and PSSM profiles," Computational biology and chemistry, 2008. 32(3): p. 227-231.
N.Q.K. Le, G. A. Sandag, and Y.-Y. Ou. "Incorporating post translational modification
information for enhancing the predictive performance of membrane transport proteins," Computational biology and chemistry 77 (2018): 251-260.
S.-A. Chen, et al., "Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties," Bioinformatics, 2011. 27(15): p. 2062-2067.
G. Zhang, et al., "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European journal of operational research, 1999. 116(1): p. 16-32.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).