SCLpred-EMS：基于深度 N 到 1 卷积神经网络的内膜系统和分泌途径蛋白的亚细胞定位预测。
SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks.
机构信息
School of Computer Science.
UCD Institute for Discovery, University College Dublin, Dublin, Ireland.
出版信息
Bioinformatics. 2020 Jun 1;36(11):3343-3349. doi: 10.1093/bioinformatics/btaa156.
MOTIVATION
The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins.
RESULTS
Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested.
AVAILABILITY AND IMPLEMENTATION
SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/.
CONTACT
动机
蛋白质的亚细胞位置可为蛋白质功能预测和药物设计提供有用信息。实验确定蛋白质的亚细胞位置是一项昂贵且耗时的任务。因此，已经开发了各种基于计算机的工具，主要使用机器学习算法来预测蛋白质的亚细胞位置。
结果
在这里，我们提出了一种基于神经网络的蛋白质亚细胞定位预测算法。我们介绍了 SCLpred-EMS，这是一种基于深度 N 到 1 卷积神经网络的集成的亚细胞定位预测器。SCLpred-EMS 将蛋白质的亚细胞位置预测为两类，即内质网系统和分泌途径与其他所有类别，马修斯相关系数为 0.75-0.86，优于我们测试的其他最先进的网络服务器。
可用性和实施
学术用户可免费在 http://distilldeep.ucd.ie/SCLpred2/ 使用 SCLpred-EMS。
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