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Lim et al., 2020 - Google Patents
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Lim et al., 2020 - Google Patents

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Lim et al., 2020

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Document ID
1867564207151755697
Author
Lim D
Blanchette M
Publication year
Publication venue
Bioinformatics

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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 …
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Classifications

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    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/22Bioinformatics, 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
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    • G06F19/14Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for phylogeny or evolution, e.g. evolutionarily conserved regions determination or phylogenetic tree construction
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    • G06F19/12Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
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