TY - JOUR
T1 - Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks
AU - Le, Nguyen Quoc Khanh
AU - Ho, Quang Thai
AU - Ou, Yu Yen
N1 - Funding Information:
This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 106-2221-E-155-068.
Funding Information:
This research is partially supported by the Ministry of Science and Technology, Taiwan , R.O.C. under Grant no. MOST 106-2221-E-155-068 .
PY - 2018/8/15
Y1 - 2018/8/15
N2 - 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.
AB - 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.
KW - Classification
KW - Deep learning
KW - DeepRab
KW - Membrane trafficking
KW - Neural networks
KW - Rab protein
UR - http://www.scopus.com/inward/record.url?scp=85048549878&partnerID=8YFLogxK
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U2 - 10.1016/j.ab.2018.06.011
DO - 10.1016/j.ab.2018.06.011
M3 - Article
C2 - 29908156
AN - SCOPUS:85048549878
SN - 0003-2697
VL - 555
SP - 33
EP - 41
JO - Analytical Biochemistry
JF - Analytical Biochemistry
ER -