Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

ABSTRACT: 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 t

SUBMITTER: Asgari E 

PROVIDER: S-EPMC4640716 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature