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Trinquier J – CQSB
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Laboratory of Computational, Quantitative and Synthetic Biology (CQSB)
UMR 7238 CNRS - Sorbonne Université
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  • Home
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    • Adaptation, Intégration, Reticulation, Evolution (AIRE)
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    • Biology of Genomes
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    • Functional Imaging Of Nuclear Architecture (FIONA)
    • Genetic Networks
    • Horizontal Evolution of Algal Lifestyles (HEAL)
    • Molecular Modeling and Drug Discovery (M2D2)
    • Proteome Diversification in Evolution
    • Statistical Genomics and Biological Physics
    • Synthetic and Systems Biology of Microalgae
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    • Functional Imaging Of Nuclear Architecture (FIONA)
    • Genetic Networks
    • Horizontal Evolution of Algal Lifestyles (HEAL)
    • Molecular Modeling and Drug Discovery (M2D2)
    • Proteome Diversification in Evolution
    • Statistical Genomics and Biological Physics
    • Synthetic and Systems Biology of Microalgae
    • Systems Engineering and Evolution Dynamics (SEED)
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    • Analytical Genomics
    • Biology of Genomes
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    • Epigenetics and RNA Metabolism in Human Diseases
    • Functional Imaging Of Nuclear Architecture (FIONA)
    • Genetic Networks
    • Horizontal Evolution of Algal Lifestyles (HEAL)
    • Molecular Modeling and Drug Discovery (M2D2)
    • Proteome Diversification in Evolution
    • Statistical Genomics and Biological Physics
    • Synthetic and Systems Biology of Microalgae
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    • Epigenetics and RNA Metabolism in Human Diseases
    • Functional Imaging Of Nuclear Architecture (FIONA)
    • Genetic Networks
    • Horizontal Evolution of Algal Lifestyles (HEAL)
    • Molecular Modeling and Drug Discovery (M2D2)
    • Proteome Diversification in Evolution
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Publications / Statistical Genomics and Biological Physics

Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems

Ciarella S., Trinquier J., Weigt M., Zamponi F. Machine Learning: Science and Technology (2023)

Comments Off on Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
1 March 2023
Publications / Statistical Genomics and Biological Physics

Efficient generative modeling of protein sequences using simple autoregressive models

Trinquier J., Uguzzoni G., Pagnani A., Zamponi F., Weigt M. Nature Communications (2021)

Comments Off on Efficient generative modeling of protein sequences using simple autoregressive models
4 October 2021
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UMR 7238 CNRS
Sorbonne Université

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Sorbonne Université

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