Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.
Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.
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Hybrid_DBP：使用混合特征和卷积神经网络预测 DNA 结合蛋白
DOI：
10.3389/fphar.2022.1031759
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发表时间：
2022
影响因子：
5.6
通讯作者：
Wu, Fangxiang
中科院分区：
文献类型：
--
作者：
Yu, Shaoyou;Peng, Dejun;Zhu, Wen;Liao, Bo;Wang, Peng;Yang, Dongxuan;Wu, Fangxiang
DNA结合蛋白（DBP）在生物体的遗传和进化中起着重要作用。特定的DNA序列可以为遗传性疾病和癌症提供潜在的治疗益处。研究这些蛋白质可以及时、有效地了解它们的作用机理，在疾病的预防和治疗中发挥独特的功能。从序列数据库中识别DNA结合蛋白成员的局限性是耗时、昂贵和无效的。因此，有效的方法来改善DBP分类是至关重要的疾病研究。在本文中，我们开发了一种新的预测器Hybrid _DBP，它通过使用混合特征和卷积神经网络来识别潜在的DBP。该方法结合MonoDiKGap和Kmer两种特征选择方法，利用MRMD2.0去除冗余特征。结果表明，DBP的正确识别率为94%，独立测试集的正确识别率达到91.2%。这意味着Hybrid_ DBP可以成为预测DBP的有用预测工具。
DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.
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影响因子：
3
作者：
Kumar M;Gromiha MM;Raghava GP
通讯作者：
Raghava GP
影响因子：
4.6
作者：
Liu B;Wang S;Wang X
通讯作者：
Wang X
DOI：
10.1016/j.gpb.2017.07.003
发表时间：
2018-03
期刊：
Genomics, proteomics & bioinformatics
影响因子：
--
作者：
Cao C;Liu F;Tan H;Song D;Shu W;Li W;Zhou Y;Bo X;Xie Z
通讯作者：
Xie Z
影响因子：
14.9
作者：
Gao, Mu;Skolnick, Jeffrey
通讯作者：
Skolnick, Jeffrey
影响因子：
4
作者：
He, Shida;Guo, Fei;Ding, Hui
通讯作者：
Ding, Hui