Machine Learning-Guided Systematic Search of DNA Sequences for Sorting Carbon Nanotubes

Autor:	Zhiwei Lin, Yoona Yang, Anand Jagota, Ming Zheng
Rok vydání:	2022
Předmět:	Machine Learning Base Sequence Nanotubes Carbon General Engineering General Physics and Astronomy General Materials Science DNA
Zdroj:	ACS Nano. 16:4705-4713
ISSN:	1936-086X 1936-0851
DOI:	10.1021/acsnano.1c11448
Popis:	The prerequisite of utilizing DNA in sequence-dependent applications is to search specific sequences. Developing a strategy for efficient DNA sequence screening represents a grand challenge due to the countless possibilities of sequence combination. Herein, relying on sequence-dependent recognition between DNA and single-wall carbon nanotubes (SWCNTs), we demonstrate a method for systematic search of DNA sequences for sorting single-chirality SWCNTs. Different from previously documented empirical search, which has a low efficiency and accuracy, our approach combines machine learning and experimental investigation. The number of resolving sequences and the success rate of finding them are improved from ∼10
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06fba181d6016759962088a2968c7702 https://doi.org/10.1021/acsnano.1c11448 Zobrazit plný text záznamu