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

研究成果: 雜誌貢獻文章同行評審

71 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)2000-2006
頁數7
期刊Journal of Computational Chemistry
38
發行號23
DOIs
出版狀態已發佈 - 9月 5 2017
對外發佈

ASJC Scopus subject areas

  • 化學 (全部)
  • 計算數學

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