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  1. Within these models, we can easily estimate both the probability of a given sequence, and, using the model's entropy, the size of the functional sequence space related to a specific protein family.
    Author:Jeanne Trinquier, Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin WeigtPublished:2021
  2. pubmed.ncbi.nlm.nih.gov

    Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 10 2 and 10 3). Furthermore, the simple ...
  3. Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform ...
    Author:Jeanne Trinquier, Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin WeigtPublished:2021
  4. Efficient generative modeling of protein sequences using simple autoregressive models Jeanne Trinquier 1,2 , Guido Uguzzoni 3,4 , Andrea Pagnani 3,4,5 , Francesco Zamponi 2 & Martin Weigt 1
    Author:Jeanne Trinquier, Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin WeigtPublished:2021
  5. our autoregressive models are the only generative mod-els we know about, which allow for calculating exact sequence probabilities, and not only non-normalized se-quence weights, thereby enabling the comparison of the same sequence in di erent models for di erent protein families. II. RESULTS A. Autoregressive models for protein families
    Author:Jeanne Trinquier, Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin WeigtPublished:2021
  6. lcqb.upmc.fr

    Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103).
  7. Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily ...
  8. semanticscholar.org

    Simple autoregressive models are proposed as highly accurate but computationally efficient generative sequence models that perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost. Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the ...
  9. Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally extremely efficient generative sequence models. We show that they ...
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