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Ciarella S, Trinquier J, Weigt M, Zamponi F. Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems. Machine Learning: Science and Technology. 4, pp.010501 (2023). |
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Gandarilla-Pérez CA, Pinilla S, Bitbol A-F, Weigt M. Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins. PLoS Comp Biol. 19(3), pp.e1011010 (2023). |
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Patteson JB, Fortinez CMarie, Putz AT, Rodriguez-Rivas J, L. Bryant H, Adhikari K, Weigt M, T. Schmeing M, Li B. Structure and Function of a Dehydrating Condensation Domain in Nonribosomal Peptide Biosynthesis. Journal of the American Chemical Society. 144(31), pp.14057 - 14070 (2022). |
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Vigué L, Croce G, Petitjean M, Ruppé E, Tenaillon O, Weigt M. Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes. Nature Communications. 13(1), pp.4030 (2022). |
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Rodriguez-Rivas J, Croce G, Muscat M, Weigt M. Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. Proceedings of the National Academy of Sciences. 119, pp. e2113118119 (2022). |
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Trinquier J, Uguzzoni G, Pagnani A, Zamponi F, Weigt M. Efficient generative modeling of protein sequences using simple autoregressive models. Nature Communications. 12(1), pp.5800 (2021). |
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Muntoni AP, Pagnani A, Weigt M, Zamponi F. adabmDCA: adaptive Boltzmann machine learning for biological sequences. BMC Bioinformatics. 22(1), pp.528 (2021). |
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Bisardi M, Rodriguez-Rivas J, Zamponi F, Weigt M. Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution. Molecular Biology and Evolution. (2021). |
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Barrat-Charlaix P, Muntoni AP, Shimagaki K, Weigt M, Zamponi F. Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families. Phys. Rev. E. 104, pp.024407 (2021). |
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Rodriguez-Horta E, Weigt M. On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins. PLOS Computational Biology. 17, pp.1-17 (2021). |
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Rodriguez-Horta E, Lage A, Weigt M, Barrat-Charlaix P. Global multivariate model learning from hierarchically correlated data. J. Stat. Mech. (2021). |
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Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KRei, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, Nikolayeva O, Nikolayeva T, Png E, Ryon KA, Sanchez JL, Shaaban H, Sierra MA, Thomas D, Young B, Abudayyeh OO, Alicea J, Bhattacharyya M, Blekhman R, Castro-Nallar E, Cañas AM, Chatziefthimiou AD, Crawford RW, De Filippis F, Deng Y, Desnues C, Dias-Neto E, Dybwad M, Elhaik E, Ercolini D, Frolova A, Gankin D, Gootenberg JS, Graf AB, Green DC, Hajirasouliha I, Hastings JJA, Hernandez M, Iraola G, Jang S, Kahles A, Kelly FJ, Knights K, Kyrpides NC, Łabaj PP, Lee PKH, Leung MHY, Ljungdahl PO, Mason-Buck G, McGrath K, Meydan C, Mongodin EF, Moraes MOzorio, Nagarajan N, Nieto-Caballero M, Noushmehr H, Oliveira M, Ossowski S, Osuolale OO, Özcan O, Paez-Espino D, Rascovan N, Richard H, Rätsch G, Schriml LM, Semmler T, Sezerman OU, et al.. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell . (2021). |
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Muntoni AP, Pagnani A, Weigt M, Zamponi F. Aligning biological sequences by exploiting residue conservation and coevolution. Phys. Rev. E. 102, pp.062409 (2020). |
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Nivina A, Grieb MSvea, Loot C, Bikard D, Cury J, Shehata L, Bernardes JS, Mazel D. Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement. Science Advances. 6, (2020). |
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Bernardes JS, Eberle RJ, Vieira FRJ, Coronado MA. A comparative pan-genomic analysis of 53 C. pseudotuberculosis strains based on functional domains. Journal of Biomolecular Structure and Dynamics. pp.1-13 (2020). |
