Abstract
The growth of biological databases has been increased in the past decades makes a protein structure prediction is one of the most important and challenging problems in Bioinformatics. The recent growth of Neural Network that have been shown promising result and also became indispensable tools in Bioinformatics. Using Convolutional Neural Network and Recurrent Neural Network with Long-Short Term Memory to predicting the secondary structure, our experiment shows a high result for RNN LSTM with accuracy 88.74% and loss rate 3.64%. In addition, for the CNN methods with an accuracy 87.74% and loss rate 3.85% prove that the latest technology of neural network also effective to be applied in secondary structure prediction of the proteins.
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1058394), and the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW) (2018-0-01865) supervised by the IITP (Institute for Information & communications Technology Promotion).
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Listio, S.W.P., Elbasani, E., Oh, TJ., Kim, B., Kim, JD. (2021). Deep Learning-Based Experimentation for Predicting Secondary Structure of Amino Acid Sequence. In: Park, J.J., Fong, S.J., Pan, Y., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 715. Springer, Singapore. https://doi.org/10.1007/978-981-15-9343-7_8
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DOI: https://doi.org/10.1007/978-981-15-9343-7_8
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