Genome annotation across species using deep convolutional neural networks.

ABSTRACT: Application of deep neural network is a rapidly expanding field now reaching many disciplines including genomics. In particular, convolutional neural networks have been exploited for identifying the functional role of short genomic sequences. These approaches rely on gathering large sets of sequences with known functional role, extracting those sequences from whole-genome-annotations. These sets are then split into learning, test and validation sets in order to train the networks. While the obtained networks perform well on validation sets, they often perform poorly when applied on whole genomes in which the ratio of positive over negative examples can be very different than in the training set. We here address this issue by assessing the genome-wide performance of networks trained with se

SUBMITTER: Khodabandelou G 

PROVIDER: S-EPMC7924482 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature