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Multiple probabilistic models extract features from protein sequence data and resolve functional diversity of very different protein families

R. Vicedomini, J.P. Bouly, E. Laine, A. Falciatore, A. Carbone
doi: https://doi.org/10.1101/717249

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doi 
https://doi.org/10.1101/717249
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  • March 9, 2021.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

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  1. R. Vicedomini1,2,*,
  2. J.P. Bouly1,3,*,
  3. E. Laine1,
  4. A. Falciatore1,3 and
  5. A. Carbone1,4,†
  1. 1Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France
  2. 2Sorbonne Université, Institut des Sciences du Calcul et des Données
  3. 3CNRS, Sorbonne Université, Institut de Biologie Physico-Chimique, Laboratory of Chloroplast Biology and Light Sensing in Microalgae - UMR7141, Paris, France
  4. 4Institut Universitaire de France, Paris 75005, France
  1. ↵†Corresponding author: alessandra.carbone{at}lip6.fr
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Posted March 09, 2021.
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Multiple probabilistic models extract features from protein sequence data and resolve functional diversity of very different protein families
R. Vicedomini, J.P. Bouly, E. Laine, A. Falciatore, A. Carbone
bioRxiv 717249; doi: https://doi.org/10.1101/717249
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