Supervised learning model predicts protein adsorption to carbon nanotubes-Reference-Cited by-同舟云学术

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Supervised learning model predicts protein adsorption to carbon nanotubes

Published:2022-01-07 Issue:1 Volume:8 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Ouassil Nicholas¹^ORCID,Pinals Rebecca L.²^ORCID,Del Bonis-O’Donnell Jackson Travis¹^ORCID,Wang Jeffrey W.¹^ORCID,Landry Markita P.¹³⁴⁵^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.

2. Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

3. Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.

4. Innovative Genomics Institute (IGI), Berkeley, CA 94720, USA.

5. California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, CA 94720, USA.

Abstract

The developed classifier predicts what proteins adsorb to nanoparticles and what protein features drive these interactions.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference83 articles.

1. Advances in the Application of Magnetic Nanoparticles for Sensing

2. Colloidal nanoparticles as advanced biological sensors

3. Advances in nanomaterials for brain microscopy

4. Nanomaterials for In Vivo Imaging

5. Engineering precision nanoparticles for drug delivery

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