Le Nguyen-Quoc-Khanh, Ho Quang-Thai, Ou Yu-Yen
Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan.
Anal Biochem. 2018 Aug 15;555:33-41. doi: 10.1016/j.ab.2018.06.011. Epub 2018 Jun 13.
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.
深度学习已越来越多地用于解决各种领域中许多具有最先进性能的问题。在生物学中，深度学习可用于减少特征提取时间并实现高水平的性能。在我们目前的工作中，我们通过二维卷积神经网络和位置特异性评分矩阵应用深度学习来对Rab蛋白分子进行分类，Rab蛋白分子是细胞内蛋白质和其他大分子转运的膜运输过程中的主要调节因子。特定Rab分子功能的功能丧失与多种人类疾病有关，例如，无脉络膜症、智力残疾、癌症。因此，创建一个精确的Rab分类模型对于帮助生物学家了解Rab的分子功能并根据此类特定人类疾病信息设计药物靶点至关重要。我们构建了一个强大的用于分类Rab的深度神经网络，对于四种特定分子功能中的每一种，其准确率分别达到了99%、99.5%、96.3%和97.6%。我们的方法展示了优于传统人工神经网络的性能。因此，从我们提出的研究中，我们既提供了一种用于分类Rab蛋白的有效工具，也为进一步研究奠定了基础，该研究可以提高使用深度神经网络进行生物建模的性能。
