Lim et al., 2020 - Google Patents
EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTMLim et al., 2020
View PDF- Document ID
- 1867564207151755697
- Author
- Lim D
- Blanchette M
- Publication year
- Publication venue
- Bioinformatics
External Links
Snippet
Motivation Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many …
- 230000001419 dependent 0 title description 31
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- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
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