Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

HIGHLIGHTS

• who: Andrea Di Gioacchino and colleagues from the Universitu00e9 de Paris, Paris, France, School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America have published the research work: Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection, in the Journal: (JOURNAL) of May/23,/2022
• what: The authors show that Restricted Boltzmann Machines (RBMs) an unsupervised two-layer neural network architecture can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin . . .
  

   

  

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