DeepTE: a computational method for de novo classification of transposons with convolutional neural network | Zendy

Haidong Yan | Zendy; Aureliano Bombarely | Zendy; Song Li | Zendy

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DeepTE: a computational method for de novo classification of transposons with convolutional neural network

Author(s) -

Haidong Yan,

Aureliano Bombarely,

Song Li

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa519

Subject(s) - convolutional neural network , transposable element , benchmarking , computer science , genome , artificial intelligence , tree (set theory) , artificial neural network , pattern recognition (psychology) , machine learning , computational biology , biology , genetics , mathematics , gene , mathematical analysis , marketing , business

Transposable elements (TEs) classification is an essential step to decode their roles in genome evolution. With a large number of genomes from non-model species becoming available, accurate and efficient TE classification has emerged as a new challenge in genomic sequence analysis.

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