Abstract
Protein secondary structure prediction has a fundamental influence on today’s bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.
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References
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman D (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search program. Nucleic Acids Res 25:3389–3402
Arjunan SV (2003) protein secondary structure prediction from amino acid sequences using a neural network classifier based on the Dempster-Shafer theory. University Technology Malaysia, Masters Thesis
Butenhof D (1997) Programming with POSIX threads, Addison-Wesley Professional Computing Series
Chandra R, Dagum L, Kohr D, Maydan D, McDonald J, Menon R (2000) Parallel programming in OpenMP. Morgan Kaufmann Publishers
Cuff J, Barton G (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Protein 34:508–519
Denoeus T (2000) A neural network classifier based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern A 30(2):131–150
Eggers S, Emer J, Levy H, Lo J, Stamm R, Tullsen D (1997) Simultaneous multithreading: a platform for next-generation processors, IEEE Micro, pp 12–18
Fedorova N, Terekhoff SA (1999) Parallel MPI implementation of training algorithms for medium-size feedforward neural networks. In: International joint conference on neural network, vol 4, June 1996, pp 2378–2379
Fathy S, Syiam M (1996) A parallel design and implementation for backpropagation neural network using MIMD architecture. In: IEEE international conference on neural networks, vol 2, June 1996, pp 1361–1366
Hua S, Sun Z (2001) A novel method of protein secondary structure prediction based on an improved support vector machines approach. J Mol Biol 308:397–407
Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA 89:10915–10919
Karp G (2002) Cell and molecular biology, 3rd edn. pp 55–57
Kim H, Park H (2003) Protein secondary structure prediction based on an improved support vector machine approach. Protein Eng 16(8):553–560
Johansson C, Lansner A (2001) A parallel implementation of a Bayesian neural network with hypercolumns. Technical Report, TRITA-NA-P0121, SANS-Nada-KTH
Liu X, Wilcox GL (April 1993) Benchmarking of the CM-5 and the Cray machines with a very large backpropagation neural network. Technical Report 93/38, University of Minnesota Supercomputer Institute, Minneapolis
Marr D, Binns F, Hill D, Hinton G, Koufaty D, Miller J, Upton M (2002) Hyper-Threading Technology architecture and microarchitecture. Intel Technol J
Qian N, Sejnowski T (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202(4):865–884
Rost B, Sander C (1994) Combining evolutionary information and neural networks to predict protein secondary structure. Protein 19:55–72
Rost B, Sander C (1992) Exercising multi-layered networks on protein secondary structure. Int J Neural Syst 3:209–220
Rost B, Sander C (1993) Prediction of secondary structure at better than 70% accuracy. J Mol Biol 232:584–599
Tian X, Bik A, Girkar M, Grey P, Saito H, Su E (2002) Intel OpenMP C++/Fortran compiler for Hyper-Threading Technology: implementation and performance. Intel Technol J 6(1)
Silva F, Alemida L (1990) Speeding up backpropagation, advanced neural computers. North-Holland, Amsterdam, pp 151–158
Suresh S, Omkar SN, Mani V (2003) Parallel implementation of memory neuron network for identification of dynamical system. Adv Vibr Eng 2(2)
Thulasiram R, Rahman RM, Thulasiraman P (2003) Neural Network Training Algorithms on Parallel Architectures for Finance Applications. In: ICPP workshops, 2003, pp 236–243
Tanomaru J, Omichi S, Azuma A (1995) General purpose MIMD computers and neural networks: three case studies. In: IEEE international conference on systems, man and cybernetics, 1995, pp 4587–4597
Weishäupl T, Schikuta E (2003) Parallelization of cellular neural networks for image processing on cluster architectures. In: International conference on parallel processing workshops. Kaohsiung, Taiwan, October 06–09, 2003
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Zhong, W., Altun, G., Tian, X. et al. Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology. J Supercomput 41, 1–16 (2007). https://doi.org/10.1007/s11227-007-0100-1
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DOI: https://doi.org/10.1007/s11227-007-0100-1