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

ABSTRACT: Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We 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 aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing mo

SUBMITTER: Di Gioacchino A 

PROVIDER: S-EPMC9553063 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature