Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks

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

研究成果: 雜誌貢獻文章

37 引文 斯高帕斯(Scopus)

摘要

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks.
原文英語
頁(從 - 到)33-41
頁數9
期刊Analytical Biochemistry
555
DOIs
出版狀態已發佈 - 八月 15 2018
對外發佈Yes

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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