Machine learning for <i>in silico</i> protein research
Article Properties
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DOI (url)
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Publication Date2024/01/01
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Journal
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Indian UGC (journal)
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Refrences118
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Citations1
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Jia-Hui Zhang
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Abstract
Cite
Zhang, Jia-Hui. “Machine Learning for <i>in silico< i> Protein Research”. Acta Physica Sinica, vol. 73, no. 6, 2024, p. 069301, https://doi.org/10.7498/aps.73.20231618.
Zhang, J.-H. (2024). Machine learning for <i>in silico</i> protein research. Acta Physica Sinica, 73(6), 069301. https://doi.org/10.7498/aps.73.20231618
Zhang, Jia-Hui. “Machine Learning for <i>in silico< i> Protein Research”. Acta Physica Sinica 73, no. 6 (2024): 069301. https://doi.org/10.7498/aps.73.20231618.
Zhang JH. Machine learning for <i>in silico</i> protein research. Acta Physica Sinica. 2024;73(6):069301.
Journal Category
Science
Physics
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Biomolecular simulation and modelling: status, progress and prospects | Journal of The Royal Society Interface |
| 60 | 2008 |
| Graph neural networks: A review of methods and applications | AI Open | 1,439 | 2020 | |
| Small molecule targeting of biologically relevant RNA tertiary and quaternary structures | Cell Chemical Biology |
| 20 | 2021 |
| Weight average approaches for predicting dynamical properties of biomolecules | Current Opinion in Structural Biology |
| 3 | 2022 |
| Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability | Nature Communications |
| 57 | 2014 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Modeling ferroelectric phase transitions with graph convolutional neural networks | Acta Physica Sinica |
| 2024 |
Citations Analysis
| Category | Category Repetition |
|---|---|
| Science: Physics | 1 |
The category
Science: Physics 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Modeling ferroelectric phase transitions with graph convolutional neural networks and was published in 2024. The most recent citation comes from a 2024 study titled Modeling ferroelectric phase transitions with graph convolutional neural networks. This article reached its peak citation in 2024, with 1 citations. It has been cited in 1 different journals. Among related journals, the Acta Physica Sinica cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.