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Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins
Author(s) -
Le NguyenQuocKhanh,
Ho QuangThai,
Ou YuYen
Publication year - 2017
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24842
Subject(s) - deep learning , convolutional neural network , artificial intelligence , computer science , field (mathematics) , machine learning , function (biology) , biology , mathematics , microbiology and biotechnology , pure mathematics
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.