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Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
October-December 2007 (vol. 4 no. 4)
pp. 572-582
ASCII Text
x 
 
Jinmiao Chen, Narendra Chaudhari, "Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 572-582, October-December, 2007.
 
 
BibTex
x
 
@article{ 10.1109/tcbb.2007.1055,
author = {Jinmiao Chen and Narendra Chaudhari},
title = {Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {4},
number = {4},
issn = {1545-5963},
year = {2007},
pages = {572-582},
doi = {http://doi.ieeecomputersociety.org/10.1109/tcbb.2007.1055},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
IS - 4
SN - 1545-5963
SP572
EP582
EPD - 572-582
A1 - Jinmiao Chen,
A1 - Narendra Chaudhari,
PY - 2007
VL - 4
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

[1] P. Baldi, S. Brunak, P. Frasconi, G. Pollastri, and G. Soda, Bidirectional IOHMMS and Recurrent Neural Networks for Protein Secondary Structure Prediction. CLUEB, 2000.
[2] J. Chen and N.S. Chaudhari, “Statistical Analysis of Long-Range Interactions in Proteins,” Proc. Int'l Conf. Bioinformatics & Computational Biology, pp. 296-302, June 2006.
[3] G.E. Crooks and S.E. Brenner, “Protein Secondary Structure: Entropy, Correlations and Prediction,” Bioinformatics, vol. 20, no. 10, pp. 1603-1611, 2004.
[4] J.A. Cuff and G.J. Barton, “Evaluation and Improvement of Multiple Sequence Methods for Protein Secondary Structure Prediction,” Proteins: Structure, Function, and Genetics, vol. 34, pp.508-519, 1999.
[5] B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap. Chapman and Hall/CRC, 1993.
[6] V.D. Francesco, J. Garnier, and P.J. Munson, “Improving Protein Secondary Structure Prediction with Aligned Homologous Sequences,” Protein Science, vol. 5, pp. 106-113, 1996.
[7] D. Frishman and P. Argos, “Knowledge-Based Protein Secondary Structure Assignment,” Proteins: Structure, Function, and Genetics, vol. 23, no. 4, pp. 566-579, 1995.
[8] J.F. Gibrat, J. Garnier, and B. Robson, “Further Developments of Protein Secondary Structure Prediction Using Information Theory,” J. Molecular Biology, vol. 198, no. 3, pp. 425-443, 1987.
[9] Y. Guermeur, G. Pollastri, A. Eliseeff, D. Zelus, H. Paugam-Moisy, and P. Baldi, “Combining Protein Secondary Structure Prediction Models with Ensemble Methods of Optimal Complexity,” Neurocomputing, vol. 56, pp. 305-327, 2004.
[10] S. Hua and Z. Sun, “A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach,” J. Molecular Biology, vol. 308, pp. 397-407, 2001.
[11] D.T. Jones, “Protein Secondary Structure Prediction Based on Position-Specific Scoring Matrices,” J. Molecular Biology, vol. 292, no. 2, pp. 195-202, 1999.
[12] W. Kabsch and C. Sander, “Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features,” Biopolymers, vol. 22, pp. 2577-2637, 1983.
[13] D. Kihara, “The Effect of Long-Range Interactions on the Secondary Structure Formation of Proteins,” Protein Science, vol. 14, pp. 1955-1963, 2005.
[14] T. Lin, B.G. Horne, P. Tino, and C.L. Giles, “Learning Long-Term Dependencies in NARX Recurrent Neural Networks,” IEEE Trans. Neural Networks, vol. 7, no. 6, pp. 1329-1337, 1996.
[15] B. Matthews, “Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme,” Biochimica et Biophysica Acta, vol. 405, no. 2, pp. 442-451, 1975.
[16] J. Moult, T. Hubbard, S.H. Bryant, K. Fidelis, and J.T. Pedersen, “Critical Assessment of Methods of Protein Structure Prediction (CASP): Round II,” Proteins: Structure, Function, and Genetics, vol. 29, pp. 2-6, 1997.
[17] K. Nagano and K. Hasegawa, “Logical Analysis of the Mechanism of Protein Folding,” J. Molecular Biology, vol. 94, pp. 257-281, 1975.
[18] G. Pollastri, “A Machine Learning Approach to Protein Structure Prediction,” PhD dissertation, Dept. of Information and Computer Science, Univ. of California, Irvine, 2003.
[19] G. Pollastri and A. McLysaght, “Porter: A New, Accurate Server for Protein Secondary Structure Prediction,” Bioinformatics, vol. 21, no. 8, pp. 1719-1720, 2005.
[20] G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, “Improving the Prediction of Protein Secondary Structure in Three and Eight Classes Using Recurrent Neural Networks and Profiles,” Proteins: Structure, Function, and Genetics, vol. 47, no. 2, pp. 228-235, 2002.
[21] G.P.S. Raghava, “Protein Secondary Structure Prediction Using Nearest Neighbor and Neural Network Approach,” Proc. Fourth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction, pp. 75-76, 2000.
[22] F.M. Richards and C.E. Kundrot, “Identification of Structural Motifs from Protein Coordinate Data: Secondary Structure and First-Level Supersecondary Structure,” Proteins: Structure, Function, and Genetics, vol. 3, no. 2, pp. 71-84, 1988.
[23] B. Rost, “PHD: Predicting One-Dimensional Protein Structure by Profile Based Neural Networks,” Methods in Enzymology, vol. 266, pp. 525-539, 1996.
[24] B. Rost and C. Sander, “Prediction of Protein Secondary Structure at Better than 70% Accuracy,” J. Molecular Biology, vol. 232, no. 2, pp. 584-599, 1993.
[25] B. Rost and C. Sander, “Combining Evolutionary Information and Neural Networks to Predict Protein Secondary Structure,” Proteins: Structure, Function and Genetics, vol. 19, no. 1, pp. 55-72, 1994.
[26] B. Rost, C. Sander, and R. Schneider, “Redefining the Goals of Protein Secondary Structure Prediction,” J. Molecular Biology, vol. 235, pp. 13-26, 1994.
[27] A.A. Salamov and V.V. Solovyev, “Prediction of Protein Secondary Structure by Combining Nearest-Neighbor Algorithms and Multiple Sequence Alignments,” J. Molecular Biology, vol. 247, no. 1, pp. 11-15, 1995.
[28] M. Schuster and K.K. Paliwal, “Bidirectional Recurrent Neural Networks,” IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2673-2681, 1997.
[29] W.R. Taylor, “Pattern Matching Methods in Protein Sequence Comparison and Structure Prediction,” Protein Eng., vol. 2, pp. 77-86, 1988.
[30] A. Zemla, C. Venclovas, K. Fidelis, and B. Rost, “A Modified Definition of SOV, a Segment-Based Measure for Protein Secondary Structure Prediction Assessment,” Proteins: Structure, Function, and Genetics, vol. 34, pp. 220-223, 1999.

Citation:
Jinmiao Chen, Narendra Chaudhari, "Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 572-582, Oct.-Dec. 2007, doi:10.1109/tcbb.2007.1055
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