hybrid-gicn
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (1.8%) to scientific vocabulary
Last synced: 6 months ago
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JSON representation
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Repository
Basic Info
- Host: GitHub
- Owner: AsifAlFaisal
- Language: Python
- Default Branch: main
- Size: 57.6 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Created about 4 years ago
· Last pushed over 2 years ago
Metadata Files
Readme
Citation
README.md
Physicochemical Properties Prediction of Chemical Substances with Hybrid-GICN
- A novel approach for predicticing physicochemical properties of chemical substances using Graph Representation Learning approach.
- For prediction, A unique, hybrid approach of graph convolutional and graph isomorphism neural network is used.
Owner
- Name: Asif Al Faisal
- Login: AsifAlFaisal
- Kind: user
- Repositories: 4
- Profile: https://github.com/AsifAlFaisal
Citation (CITATION.cff)
cff-version: 1.1.0 message: "If you use this software, please cite it as below." authors: - family-names: "Faisal" given-names: "Asif Al" orcid: "https://orcid.org/0000-0002-3284-4617" title: "Physicochemical Properties Prediction of Chemical Substances with Hybrid-GICN" version: 1.0 doi: 10.5281/zenodo.8024539 date-released: 2023-06-11 url: "https://github.com/AsifAlFaisal/hybrid-gicn"