Machine learning-integrated omics for the risk and safety assessment of nanomaterials-Reference-Cited by-同舟云学术

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

Published:2021 Issue:5 Volume:9 Page:1598-1608
ISSN:2047-4830
Container-title:Biomaterials Science
language:en
Short-container-title:Biomater. Sci.

Author:

Ahmad Farooq¹²³⁴⁵^ORCID,Mahmood Asif⁶⁷⁸⁹¹⁰^ORCID,Muhmood Tahir¹¹¹²¹³¹⁴¹⁰^ORCID

Affiliation:

1. College of Engineering and Applied Sciences

2. Nanjing National Laboratory of Microstructures

3. Jiangsu Key Laboratory of Artificial Functional Materials

4. Nanjing University

5. Nanjing

6. Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials

7. School of Chemistry and Chemical Engineering

8. Beijing Institute of Technology

9. Beijing

10. China

11. State Key Lab of Metal Matrix Composites

12. School of Materials Science and Engineering

13. Shanghai Jiao Tong University

14. Shanghai

Abstract

Omics data processed by machine learning algorithms to characterize endotypes for the autonomous comparison of safety and risk assessment of nanomaterials for preclinical safety assessment and post-marketing vigilance and decision making.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Biomedical Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2021/BM/D0BM01672A

Reference109 articles.

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4. Multi-spectroscopic and molecular modeling studies to reveal the interaction between propyl acridone and calf thymus DNA in the presence of histone H1: binary and ternary approaches

5. New insights into the binding behavior of lomefloxacin and human hemoglobin using biophysical techniques: binary and ternary approaches

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