Faculty of Engineering, Arab Academy for Science and Technology and Maritime Transport, Alexandria, Egypt.
Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt.
Comput Biol Chem. 2021 Dec;95:107584. doi: 10.1016/j.compbiolchem.2021.107584. Epub 2021 Sep 24.
Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learning have been shown to predict the proteins' functions. This paper proposes a hybrid deep neural network model to predict an unknown protein's functions from sequences. The proposed model is named Deep_CNN_LSTM_GO. Deep_CNN_LSTM_GO is an Integration between Convolutional Neural network (CNN) and Long Short-Term Memory (LSTM) Neural Network to learn features from amino acid sequences and outputs the three different Gene Ontology (GO). The gene ontology represents the protein functions in the three sub-ontologies: Molecular Functions (MF), Biological Process (BP), and Cellular Component (CC). The proposed model has been trained and tested using UniProt-SwissProt's dataset. Another test has been done using Computational Assessment of Function Annotation (CAFA) on the three sub-ontologies. The proposed model outperforms different methods proposed in the field with better performance using three different evaluation metrics (Fmax, Smin, and AUPR) in the three sub-ontologies (MF, BP, CC).
蛋白质的氨基酸序列可用于确定蛋白质的功能。然而，确定单个蛋白质的功能需要许多资源和大量的时间。深度学习等计算智能方法已被证明可以预测蛋白质的功能。本文提出了一种混合深度神经网络模型，用于从序列中预测未知蛋白质的功能。所提出的模型被命名为 Deep_CNN_LSTM_GO。Deep_CNN_LSTM_GO 是卷积神经网络 (CNN) 和长短期记忆 (LSTM) 神经网络的集成，用于从氨基酸序列中学习特征，并输出三个不同的基因本体论 (GO)。基因本体论以三个子本体论（分子功能 (MF)、生物过程 (BP) 和细胞成分 (CC)）来表示蛋白质的功能。该模型使用 UniProt-SwissProt 的数据集进行了训练和测试。在三个子本体论上还使用计算功能注释评估 (CAFA) 进行了另一个测试。该模型在三个子本体论 (MF、BP、CC) 中使用三个不同的评估指标 (Fmax、Smin 和 AUPR) 表现优于该领域提出的不同方法。
