Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins-Reference-Cited by-同舟云学术

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Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins

Published:2024-08-26 Issue:35 Volume:128 Page:8423-8436
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Hu Guanghao¹,Moon Jooa¹,Hayashi Tomohiro¹²^ORCID

Affiliation:

1. Department of Materials Science and Engineering, School of Materials Science and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama-shi, Kanagawa-ken 226-8502, Japan

2. The Institute for Solid State Physics, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-0882, Japan

Funder

Ministry of Education, Culture, Sports, Science and Technology

Japan Society for the Promotion of Science

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.4c02461

Reference73 articles.

1. Predicting Protein Subcellular Localization: Past, Present, and Future

2. Protein subcellular location prediction

3. Drug carrier interaction with blood: a critical aspect for high-efficient vascular-targeted drug delivery systems

4. Close-Range Electrostatic Interactions in Proteins

5. Cellular crowding imposes global constraints on the chemistry and evolution of proteomes

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