ProtVec：生物序列的连续分布表示
ProtVec: A Continuous Distributed Representation of Biological Sequences
摘要：
提出了一种新的生物序列表示和特征提取方法。命名为bio-vectors（BioVec）是指生物序列，蛋白质（氨基酸序列）是蛋白质载体（ProtVec），基因序列是基因载体（GeneVec），这种表示法可以广泛用于蛋白质组学和基因组学中的深度学习应用。在本文中，我们专注于蛋白质载体，可用于广泛的生物信息学研究，如家庭分类，蛋白质可视化，结构预测，无序蛋白质鉴定，和蛋白质相互作用预测。在该方法中，我们采用人工神经网络的方法，并表示一个蛋白质序列与一个单一的密集的n维向量。为了评估该方法，我们将其应用于从Swiss-Prot获得的属于7，027个蛋白质家族的324，018个蛋白质序列的分类，其中获得了93% ± 0.06%的平均家族分类准确率，优于现有的家族分类方法。此外，我们使用ProtVec表示从结构蛋白质预测无序蛋白质。使用两个无序序列数据库：DisProt数据库以及具有富含苯丙氨酸-甘氨酸重复序列（FG-NUPS）的核孔蛋白的无序区域的数据库。使用支持向量机分类器，FG-Nup序列与蛋白质数据库（PDB）中发现的结构化蛋白质序列区分的准确率为99.8%，非结构化DisProt序列与结构化DisProt序列区分的准确率为100.0%。这些结果表明，通过仅将各种蛋白质的序列数据提供到该模型中，可以确定关于蛋白质结构的准确信息。
英文摘要：
We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%+-0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
