DEEPre: sequence-based enzyme EC number prediction by deep learning.
DEEPre: sequence-based enzyme EC number prediction by deep learning.
复制标题
DEEPre：通过深度学习进行基于序列的酶 EC 数预测
DOI：
10.1093/bioinformatics/btx680
复制
发表时间：
2018-03-01
期刊：
影响因子：
--
通讯作者：
Gao X
中科院分区：
文献类型：
--
作者：
Li Y;Wang S;Umarov R;Xie B;Fan M;Li L;Gao X
酶功能注释具有广泛的应用，例如宏基因组学、工业生物技术以及酶缺乏引起的疾病的诊断。然而，通过实验确定每种酶的功能所需的时间和资源使得成本过高。因此，计算酶功能预测变得越来越重要。在本文中，我们开发了这样一种方法，通过预测酶委员会数来确定酶的功能。我们在酶功能预测领域提出了一种端到端的特征选择和分类模型训练方法，以及一种自动且鲁棒的特征维数均匀化方法DEEPre。我们的模型没有从酶序列中提取手工制作的特征，而是将原始序列编码作为输入，根据分类结果从原始编码中提取卷积和序列特征，以直接提高预测性能。在两个大型数据集上进行的彻底的交叉验证实验表明，DEEPre 比以前最先进的方法提高了预测性能。此外，我们的服务器在确定单独的低同源性数据集上的酶的主要类别方面优于其他五个服务器。两个案例研究证明了 DEEPre 能够捕获酶亚型的功能差异。该服务器可以通过 http://www.cbrc.kaust.edu.sa/DEEPre 免费访问。 补充数据可在生物信息学在线获取。
Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre’s ability to capture the functional difference of enzyme isoforms. The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Supplementary data are available at Bioinformatics online.
登录
查看更多内容
DOI：
10.1093/bioinformatics/btx480
发表时间：
2017-11-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Dai H;Umarov R;Kuwahara H;Li Y;Song L;Gao X
通讯作者：
Gao X
影响因子：
7.5
作者：
Carmena M;Ruchaud S;Earnshaw WC
通讯作者：
Earnshaw WC
影响因子：
5.8
作者：
Davies, Matthew N.;Secker, Andrew;Flower, Darren R.
通讯作者：
Flower, Darren R.
影响因子：
5.8
作者：
Chou, KC
通讯作者：
Chou, KC
影响因子：
14.9
作者：
Bairoch, A;Apweiler, R
通讯作者：
Apweiler, R