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MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy
Journal of Bioinformatics and Computational Biology ( IF 1 ) Pub Date : 2019-11-25 , DOI: 10.1142/s0219720019400158
Chun Fang 1 , Yoshitaka Moriwaki 2 , Caihong Li 1 , Kentaro Shimizu 2
Affiliation  

Molecular recognition features (MoRFs) usually act as “hub” sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/MoRFPred_en.php .

中文翻译:

MoRFPred_en:使用集成学习策略对 MoRF 进行基于序列的预测

分子识别特征 (MoRF) 通常在本质上无序蛋白 (IDP) 的相互作用网络中充当“枢纽”位点。由于越来越多的严重疾病被发现与无序蛋白质有关,因此识别 MoRF 变得越来越重要。在这项研究中,我们提出了一种名为 MoRFPred_en 的集成学习策略,用于从蛋白质序列中预测 MoRF。该方法结合了四个利用不同序列派生特征进行预测的子模型,包括基于多通道一维卷积神经网络 (CNN_1D 多通道) 的模型、两个基于深度二维卷积神经网络 (DCNN_2D) 的模型和支持向量基于机器(SVM)的模型。与同一数据集上的其他方法相比,MoRFPred_en 方法比现有最先进的 MoRF 预测方法产生了更好的结果,在 VALIDATION419 数据集上实现了 0.762 的 AUC,在 TEST45 数据集上实现了 0.795,在 TEST49 数据集上实现了 0.776。可用性:http://vivace.bi.au-tokyo.ac.jp:8008/fang/MoRFPred_en.php。
更新日期:2019-11-25
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