SENSE: Siamese neural network for sequence embedding and alignment-free comparison
Abstract
Sequence analysis is arguably a foundation of modern biology. Classic approaches to sequence analysis are based on sequence alignment, which is limited when dealing with large-scale sequence data. A dozen of alignment-free approaches have been developed to provide computationally efficient alternatives to alignment-based approaches. However, existing methods define sequence similarity based on various heuristics and can only provide rough...
Paper Details
Title
SENSE: Siamese neural network for sequence embedding and alignment-free comparison
Published Date
Oct 22, 2018
Journal
Volume
35
Issue
11
Pages
1820 - 1828
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