Open AccesstRNA-DL: A Deep Learning Approach to Improve tRNAscan-SE Prediction Results
Author(s) -
Xin Gao,
Zhi Wei,
Hákon Hákonarson
Publication year - 2018
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000493215
Subject(s) - deep learning , artificial intelligence , convolutional neural network , transfer rna , deep sequencing , transfer of learning , computer science , artificial neural network , false positive paradox , computational biology , deep neural networks , machine learning , feature (linguistics) , biology , genetics , gene , rna , genome , linguistics , philosophy
tRNAscan-SE is the leading tool for transfer RNA (tRNA) annotation, which has been widely used in the field. However, tRNAscan-SE can return a significant number of false positives when applied to large sequences. Recently, conventional machine learning methods have been proposed to address this issue, but their efficiency can be still limited due to their dependency on handcrafted features. With the growing availability of large-scale genomic data-sets, deep learning methods, especially convolutional neural networks, have demonstrated excellent power in characterizing sequence patterns in genomic sequences. Thus, we hypothesize that deep learning may bring further improvement for tRNA prediction.