Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins

5 years ago

Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins

Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou

In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. In this study, we approach a deep learning technique generated from convolutional neural networks and position specific scoring matrix to identify the electron transport proteins, which is very important biological function. Our proposed technique can serve as a powerful tool for biologists to identify electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/jcc.24842