Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains

Taub, Ronen; Wasserman, Tanya; Savir, Yonatan

doi:10.1109/cibcb56990.2023.10264889

2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2023

DOI: 10.1109/cibcb56990.2023.10264889

|View full text |Cite

Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains

Ronen Taub,

Tanya Wasserman,

Yonatan Savir

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

da Silva,

Ferreira,

Braga

et al. 2024

Information

View full text Add to dashboard Cite

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help identify emerging trends. This study aims to map GNN applications, research directions, and key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals a rising trend in GNN publications, especially since 2018. Computer Science, Engineering, and Telecommunications play significant roles in GNN research, with a focus on deep learning, graph convolutional networks, neural networks, convolutional neural networks, and machine learning. China and the USA combined account for 76.4% of the publications. Chinese universities concentrate on graph convolutional networks, deep learning, feature extraction, and task analysis, whereas American universities focus on machine learning and deep learning. The study also highlights the importance of Chemistry, Physics, Mathematics, Imaging Science & Photographic Technology, and Computer Science in their respective knowledge communities. In conclusion, the bibliometric analysis provides an overview of GNN research, showing growing interest and applications across various disciplines, and highlighting the potential of GNNs in solving complex problems and the need for continued research and collaboration.

show abstract

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

da Silva,

Ferreira,

Braga

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains

Cited by 1 publication

References 18 publications

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Contact Info

Product

Resources

About