Towards parsimonious generative modeling of RNA families
Calvanese F., Lambert C., Nghe P., Zamponi F., Weigt M. Nucleic Acids Research (2024)
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10 June 2024
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)
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1 March 2023
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution
Bisardi M., Rodriguez-Rivas J., Zamponi F., Weigt M. Molecular Biology and Evolution (2022)
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7 January 2022
adabmDCA: adaptive Boltzmann machine learning for biological sequences
Muntoni A., Pagnani A., Weigt M., Zamponi F. BMC Bioinformatics (2021)
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1 December 2021
Efficient generative modeling of protein sequences using simple autoregressive models
Trinquier J., Uguzzoni G., Pagnani A., Zamponi F., Weigt M. Nature Communications (2021)
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4 October 2021
Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families
Barrat-Charlaix P., Muntoni A., Shimagaki K., Weigt M., Zamponi F. Physical Review E (2021)
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6 August 2021
Aligning biological sequences by exploiting residue conservation and coevolution
Muntoni A., Pagnani A., Weigt M., Zamponi F. Physical Review E (2020)
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7 December 2020