DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment
DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment
复制标题
DeepPred-SubMito：一种基于多通道卷积神经网络和数据集平衡处理的新型线粒体定位预测器
DOI：
10.3390/ijms21165710
复制
发表时间：
2020-08
影响因子：
5.6
通讯作者：
Qiuwen Zhang
中科院分区：
文献类型：
--
作者：
Xiao Wang;Yinping Jin;Qiuwen Zhang
Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for disease pathogenesis and drug design. A mitochondrion has four submitochondrial compartments, the matrix, the outer membrane, the inner membrane, and the intermembrane space, but various existing studies ignored the intermembrane space. The majority of researchers used traditional machine learning methods for predicting mitochondrial protein localization. Those predictors required expert-level knowledge of biology to be encoded as features rather than allowing the underlying predictor to extract features through a data-driven procedure. Besides, few researchers have considered the imbalance in datasets. In this paper, we propose a novel end-to-end predictor employing deep neural networks, DeepPred-SubMito, for protein submitochondrial location prediction. First, we utilize random over-sampling to decrease the influence caused by unbalanced datasets. Next, we train a multi-channel bilayer convolutional neural network for multiple subsequences to learn high-level features. Third, the prediction result is outputted through the fully connected layer. The performance of the predictor is measured by 10-fold cross-validation and 5-fold cross-validation on the SM424-18 dataset and the SubMitoPred dataset, respectively. Experimental results show that the predictor outperforms state-of-the-art predictors. In addition, the prediction of results in the M983 dataset also confirmed its effectiveness in predicting submitochondrial locations.
登录
查看更多内容
DOI：
--
发表时间：
2015-02
期刊：
ArXiv
影响因子：
--
作者：
Andrew J. R. Simpson
通讯作者：
Andrew J. R. Simpson
使用深度卷积和循环神经网络预测 RNA-蛋白质序列和结构结合偏好
影响因子：
4.4
作者：
Pan X;Rijnbeek P;Yan J;Shen HB
通讯作者：
Shen HB
影响因子：
2.2
作者：
Yousef，Malik;Allmer，Jens
通讯作者：
Allmer，Jens
影响因子：
2
作者：
Qiu, Wenying;Li, Shan;Yu, Bin
通讯作者：
Yu, Bin
DOI：
--
发表时间：
1998-08
期刊：
--
影响因子：
--
作者：
C. Ling;Chenghui Li
通讯作者：
C. Ling;Chenghui Li