RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors
RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors
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DOI：
10.1109/tcbb.2017.2773063
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发表时间：
2018-11-01
影响因子：
4.5
通讯作者：
Dukka, B. K. C.
中科院分区：
文献类型：
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作者：
Ismail, Hamid D.;Saigo, Hiroto;Dukka, B. K. C.
核受体（NR）超家族在关键的生物学、发育和生理过程中起着重要作用。开发NR蛋白的分类方法是理解新发现的NR蛋白的结构和功能的重要一步。目前关于NR分类的研究要么不能达到最佳的准确性，要么不是针对所有已知的NR亚族。在这项研究中，我们开发了RF-NR，这是一种基于随机森林的方法，用于改进核受体的分类。RF-NR可以预测查询蛋白质序列是否属于八个NR亚家族之一或它是非NR序列。RF-NR使用类似光谱的特征，即：氨基酸组成、二肽组成和三肽组成。在具有不同序列冗余减少标准的两个独立数据集上进行基准测试，RF-NR实现了比其他现有方法更好（或相当）的准确性。我们的方法的额外优势是，我们还可以获得有关NR亚家族分类所需的重要特征的生物学见解。RF-NR可在http://bcb.ncat.edu/RF_NR/上免费获得。
The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental, and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study, we developed RF-NR which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition, and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies. RF-NR is freely available at http://bcb.ncat.edu/RF_NR/.