The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.
The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.
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通过机器学习表征水通道蛋白的结构并预测肿瘤进展
DOI：
10.3389/fcell.2022.845622
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发表时间：
2022
影响因子：
5.5
通讯作者：
Su X
中科院分区：
文献类型：
--
作者：
Chen Z;Jiao S;Zhao D;Zou Q;Xu L;Zhang L;Su X
癌症的复发和新发病例构成了一个具有挑战性的人类健康问题。水通道蛋白（Aquaporins, AQPs）可在多种肿瘤中表达，包括脑、乳腺、胰腺、结肠、皮肤、卵巢、肺等，肿瘤的组织学分级与AQP表达呈正相关。因此，水通道蛋白的鉴定是一个有待探索的领域。计算工具在水通道蛋白鉴定中起着重要的作用。在这项研究中，我们提出了可靠、准确和自动化的序列预测器iAQPs-RF来识别AQPs。在本研究中，特征提取方法为188D （global protein sequence descriptor， GPSD）。采用随机森林（RF）、朴素贝叶斯（NB）、支持向量机（SVM）、XGBoost、逻辑回归（LR）和决策树（DT） 6种常用分类器对AQP进行分类。分类结果表明，随机森林（RF）算法是最合适的机器学习算法，准确率为97.689%。采用方差分析（ANOVA）对这些特征进行分析。采用基于方差分析方法和IFS策略的特征排序来搜索最优特征。分类结果表明，第26个特征（中性/疏水）和第21个特征（疏水）是区分aqp与非aqp的最有力和最具信息量的两个特征。以往的研究报道了质膜蛋白具有疏水特性。水通道蛋白亚细胞定位预测表明，所有水通道蛋白均为质膜蛋白，具有高度保守的跨膜结构。此外，水通道蛋白的三维结构与定位结果一致。因此，这些研究证实了水通道蛋白具有疏水性。虽然水通道蛋白是高度保守的跨膜结构，但系统发育树显示了水通道蛋白在进化过程中的多样性。主成分分析表明，54D特征能很好地分离阳性和阴性样品，说明54D特征能有效地对水通道蛋白进行分类。在线预测服务器可在http://lab.malab.cn/ ~ acy/iAQP访问。
Recurrence and new cases of cancer constitute a challenging human health problem. Aquaporins (AQPs) can be expressed in many types of tumours, including the brain, breast, pancreas, colon, skin, ovaries, and lungs, and the histological grade of cancer is positively correlated with AQP expression. Therefore, the identification of aquaporins is an area to explore. Computational tools play an important role in aquaporin identification. In this research, we propose reliable, accurate and automated sequence predictor iAQPs-RF to identify AQPs. In this study, the feature extraction method was 188D (global protein sequence descriptor, GPSD). Six common classifiers, including random forest (RF), NaiveBayes (NB), support vector machine (SVM), XGBoost, logistic regression (LR) and decision tree (DT), were used for AQP classification. The classification results show that the random forest (RF) algorithm is the most suitable machine learning algorithm, and the accuracy was 97.689%. Analysis of Variance (ANOVA) was used to analyse these characteristics. Feature rank based on the ANOVA method and IFS strategy was applied to search for the optimal features. The classification results suggest that the 26th feature (neutral/hydrophobic) and 21st feature (hydrophobic) are the two most powerful and informative features that distinguish AQPs from non-AQPs. Previous studies reported that plasma membrane proteins have hydrophobic characteristics. Aquaporin subcellular localization prediction showed that all aquaporins were plasma membrane proteins with highly conserved transmembrane structures. In addition, the 3D structure of aquaporins was consistent with the localization results. Therefore, these studies confirmed that aquaporins possess hydrophobic properties. Although aquaporins are highly conserved transmembrane structures, the phylogenetic tree shows the diversity of aquaporins during evolution. The PCA showed that positive and negative samples were well separated by 54D features, indicating that the 54D feature can effectively classify aquaporins. The online prediction server is accessible at http://lab.malab.cn/∼acy/iAQP.
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影响因子：
10.7
作者：
Minh BQ;Nguyen MA;von Haeseler A
通讯作者：
von Haeseler A
影响因子：
14.9
作者：
Bhardwaj N;Langlois RE;Zhao G;Lu H
通讯作者：
Lu H
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
影响因子：
3.7
作者：
Chae YK;Woo J;Kim MJ;Kang SK;Kim MS;Lee J;Lee SK;Gong G;Kim YH;Soria JC;Jang SJ;Sidransky D;Moon C
通讯作者：
Moon C
iLearn：一个集成平台和元学习器，用于 DNA、RNA 和蛋白质序列数据的特征工程、机器学习分析和建模
影响因子：
9.5
作者：
Chen, Zhen;Zhao, Pei;Song, Jiangning
通讯作者：
Song, Jiangning