References
[1]. Gardner, M. W., & Dorling, S. R. “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences.” Atmospheric environment, 32(14-15), 2627-2636. (1998).
[2]. Singh, J., and Banerjee, R. “A study on single and multi-layer perceptron neural network.” In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) 35-40 (2019)
[3]. Freund, Y., Schapire, R. E. "Large margin classification using the perceptron algorithm." Machine Learning 37(3) 277–296 (1999).
[4]. LeCun, Y., Bengio, Y., and Hinton, G. “Deep learning.” nature, 521(7553), 436-444 (2015).
[5]. Farabet, C., Couprie, C., Najman, L. and LeCun, Y. “Learning hierarchical features for scene labeling.” IEEE Trans. Pattern Anal. Mach. Intell 35, 1915–1929 (2013).
[6]. Hinton, G. et al. “Deep neural networks for acoustic modeling in speech recognition.” IEEE Signal Processing Magazine 29, 82–97 (2012).
[7]. Doan, S., Conway, M., Phuong, T. M., and Ohno-Machado, L. “Natural language processing in biomedicine: a unified system architecture overview.” Clinical bioinformatics, 275-294 (2014).
[8]. Helmstaedter, M. et al. “Connectomic reconstruction of the inner plexiform layer in the mouse retina.” Nature 500, 168–174 (2013).
[9]. Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., and Ahsan, M. J. “Machine learning in drug discovery: a review.” Artificial Intelligence Review, 55(3), 1947-1999 (2022).
[10]. Narayanan, A., Keedwell, E. C., and Olsson, B. “Artificial intelligence techniques for bioinformatics.” Applied bioinformatics, 1, 191-222 (2002)
[11]. Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A. et al. “Deep learning enables rapid identification of potent DDR1 kinase inhibitors.” Nat Biotechnol 37, 1038–1040 (2019).
[12]. Miglani, A., and Kumar, N. “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges.” Vehicular Communications, 20 100-114 (2019).
Cite this article
Zhu,H. (2023). Multi-layered perceptron and its applications in biotechnology. Theoretical and Natural Science,20,159-165.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Gardner, M. W., & Dorling, S. R. “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences.” Atmospheric environment, 32(14-15), 2627-2636. (1998).
[2]. Singh, J., and Banerjee, R. “A study on single and multi-layer perceptron neural network.” In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) 35-40 (2019)
[3]. Freund, Y., Schapire, R. E. "Large margin classification using the perceptron algorithm." Machine Learning 37(3) 277–296 (1999).
[4]. LeCun, Y., Bengio, Y., and Hinton, G. “Deep learning.” nature, 521(7553), 436-444 (2015).
[5]. Farabet, C., Couprie, C., Najman, L. and LeCun, Y. “Learning hierarchical features for scene labeling.” IEEE Trans. Pattern Anal. Mach. Intell 35, 1915–1929 (2013).
[6]. Hinton, G. et al. “Deep neural networks for acoustic modeling in speech recognition.” IEEE Signal Processing Magazine 29, 82–97 (2012).
[7]. Doan, S., Conway, M., Phuong, T. M., and Ohno-Machado, L. “Natural language processing in biomedicine: a unified system architecture overview.” Clinical bioinformatics, 275-294 (2014).
[8]. Helmstaedter, M. et al. “Connectomic reconstruction of the inner plexiform layer in the mouse retina.” Nature 500, 168–174 (2013).
[9]. Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., and Ahsan, M. J. “Machine learning in drug discovery: a review.” Artificial Intelligence Review, 55(3), 1947-1999 (2022).
[10]. Narayanan, A., Keedwell, E. C., and Olsson, B. “Artificial intelligence techniques for bioinformatics.” Applied bioinformatics, 1, 191-222 (2002)
[11]. Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A. et al. “Deep learning enables rapid identification of potent DDR1 kinase inhibitors.” Nat Biotechnol 37, 1038–1040 (2019).
[12]. Miglani, A., and Kumar, N. “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges.” Vehicular Communications, 20 100-114 (2019).