Using Deep Learning with Position Specific Scoring Matrices to Identify Efflux Proteins in Membrane and Transport Proteins
2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE) (2016)
Oct. 31, 2016 to Nov. 2, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BIBE.2016.69
In several years, deep learning is a new area of machine learning field, which is the motivation of developing machine learning near to artificial intelligent. The neural networks belongs to deep learning are progressively important ideas in a variety of fields with great performance. Accordingly, utilization of deep learning in bioinformatics to enhance performance is very important. Convolutional neural networks is a network of deep learning which is claimed to be the best model to solve the problem of object recognition and detection utilizing GPU computing. In this study, we try to use CNN to identify efflux proteins in membrane and transport proteins, which is a famous problem in bioinformatics field. We construct the CNN from PSSM profiles with CUDA and Keras package based on Theano backend. Finally this approach achieved a significant improvement after we compare with the previous paper on efflux proteins. The proposed method can serve as an effective tool for identifying efflux proteins and can help biologists understand the functions of the efflux proteins. Moreover this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics.
Proteins, Biological neural networks, Biomembranes, Machine learning, Artificial neural networks, Two dimensional displays, Shape
S. W. Taju, N. Le and Y. Ou, "Using Deep Learning with Position Specific Scoring Matrices to Identify Efflux Proteins in Membrane and Transport Proteins," 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, 2016, pp. 101-108.