Abstract
As the basis and key of cell activities, protein plays an important role in many life activities. Protein usually does not work alone. Under normal circumstances, most proteins perform specific functions by interacting with other proteins, and play the greatest role in life activity. The prediction of protein-protein interaction (PPI) is a very basic and important research in bioinformatics. PPI controls a large number of cell activities and is the basis of most cell activities. It provides a very important theoretical basis and support for disease prevention and treatment, and drug development. Because experimental methods are slow and expensive, methods based on machine learning are gradually being applied to PPI problems. We propose a new model called BiLSTM-RF, which can effectively predict PPI.
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Acknowledgement
This work is supported by the fundamental Research Funds for the Central Universities, 2020QN89, Xuzhou science and technology plan project (KC19142), the talent project of ‘Qingtan scholar’ of Zaozhuang University, Jiangsu Provincial Natural Science Foundation, China (SBK2019040953), Youth Innovation Team of Scientific Research Foundation of the Higher Education Institutions of Shandong Province, China (2019KJM006), the Key Research Program of the Science Foundation of Shandong Province (ZR2020KE001), the PhD research startup foundation of Zaozhuang University (2014BS13) and Zaozhuang University Foundation (2015YY02), the Natural Science Foundation of China (61902337), Natural Science Fund for Colleges and Universities in Jiangsu Province (19KJB520016), Xuzhou Natural Science Foundation KC21047 and Young talents of science and technology in Jiangsu.
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Tao, Z. et al. (2022). Prediction Protein-Protein Interactions with LSTM. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. SIMUtools 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_41
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DOI: https://doi.org/10.1007/978-3-030-97124-3_41
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