MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints-Reference-Cited by-同舟云学术

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints

Published:2021-01-27 Issue:1 Volume:13 Page:25-33
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Li Shimeng,Zhang Li,Feng Huawei,Meng Jinhui,Xie Di,Yi Liwei,Arkin Isaiah T.,Liu Hongsheng^ORCID

Funder

Key Technology Research and Development Program of Shandong

Key Technologies Research and Development Program

Department of Education of Liaoning Province

Youth Science Foundation of Lanzhou Jiaotong University

Transformation Program of Scientific and Technological Achievements of Jiangsu Provence

Program for Nonferrous Metals Vacuum Metallurgy Innovation Team of Ministry of Science and Technology

Large-scale Instrument Equipment Sharing Service Plat-form Capacity Building Fund

High-level innovation team foreign training project

Shenyang Science and Technology Bureau

Shenyang Science and Technology Plan Project

National Science and Technology Planning Project

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

http://link.springer.com/content/pdf/10.1007/s12539-020-00407-2.pdf

Reference47 articles.

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4. Hillebrecht A, Muster W, Brigo A, Kansy M, Weiser T, Singer T (2011) Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations. Chem Res Toxicol 24(6):843–854. https://doi.org/10.1021/tx2000398

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