Predicting the host of influenza viruses based on the word vector.
Predicting the host of influenza viruses based on the word vector.
复制标题
基于词向量预测流感病毒宿主
作者：
Xu B;Tan Z;Li K;Jiang T;Peng Y
新兴的流感病毒继续威胁到公共卫生。快速确定新发现的流感病毒的宿主范围将有助于早期评估其风险。在这里，我们尝试使用基于单词矢量的支持向量机（SVM）分类器来预测流感病毒的宿主，这是一种新的表示和生物序列的特征提取方法。结果表明，单词矢量，序列类型（DNA或蛋白质）中的单词长度以及衍生序列的物种来产生矢量，都影响了模型的性能，以预测流感的流感病毒宿主。在几乎所有情况下，建立在表面蛋白质蛋白上血凝素（HA）和神经氨酸酶（Na）（或其基因）的模型中，其结果比内部流感蛋白（或其基因）更好。当模型基于基因基于HA基因（基于单词矢量（三个字母的长））建立的模型时，可以实现最佳性能。这导致鸟类的准确性为99.7％，人类的精度为96.9％，猪流感病毒的准确性为90.6％。与使用基本局部对齐搜索工具（BLAST）的序列同源性最佳搜索方法相比，基于矢量的单词模型仍然需要进一步改进，以预测流感的A型病毒。
Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences. The results show that the length of the word within the word vector, the sequence type (DNA or protein) and the species from which the sequences were derived for generating the word vector all influence the performance of models in predicting the host of influenza viruses. In nearly all cases, the models built on the surface proteins hemagglutinin (HA) and neuraminidase (NA) (or their genes) produced better results than internal influenza proteins (or their genes). The best performance was achieved when the model was built on the HA gene based on word vectors (words of three-letters long) generated from DNA sequences of the influenza virus. This results in accuracies of 99.7% for avian, 96.9% for human and 90.6% for swine influenza viruses. Compared to the method of sequence homology best-hit searches using the Basic Local Alignment Search Tool (BLAST), the word vector-based models still need further improvements in predicting the host of influenza A viruses.
登录
查看更多内容
DOI：
10.1038/nrmicro.2016.87
发表时间：
2016-08
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
Te Velthuis AJ;Fodor E
通讯作者：
Fodor E
影响因子：
6.7
作者：
Tong S;Zhu X;Li Y;Shi M;Zhang J;Bourgeois M;Yang H;Chen X;Recuenco S;Gomez J;Chen LM;Johnson A;Tao Y;Dreyfus C;Yu W;McBride R;Carney PJ;Gilbert AT;Chang J;Guo Z;Davis CT;Paulson JC;Stevens J;Rupprecht CE;Holmes EC;Wilson IA;Donis RO
通讯作者：
Donis RO
影响因子：
3.7
作者：
ElHefnawi, Mahmoud;Sherif, Fayroz E.
通讯作者：
Sherif, Fayroz E.
影响因子：
168.9
作者：
Claas, ECJ;Osterhaus, ADME;Webster, RG
通讯作者：
Webster, RG
影响因子：
4.3
作者：
Tamuri AU;Dos Reis M;Hay AJ;Goldstein RA
通讯作者：
Goldstein RA