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.

介绍了一种新的生物序列表示和特征提取方法 BioVec，通过人工神经网络方法，将蛋白质序列表示为单个 n 维向量，可精确地识别蛋白质家族分类、蛋白质结构预测、无序蛋白质鉴定等各类生物信息学任务，表现优于现有方法。