IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design

Ali, Farman; Khalid, Majdi; Almuhaimeed, Abdullah; Masmoudi, Atef; Alghamdi, Wajdi; Yafoz, Ayman

doi:10.1016/j.jocs.2024.102388

Journal of Computational Science

2024

DOI: 10.1016/j.jocs.2024.102388

|View full text |Cite

IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design

Farman Ali,

Majdi Khalid,

Abdullah Almuhaimeed

et al.

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...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction

Almusallam,

Ali,

Kumar

et al. 2024

Results in Engineering

View full text Add to dashboard Cite

Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction

Almusallam,

Ali,

Kumar

et al. 2024

Results in Engineering

View full text Add to dashboard Cite

Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy

Zouari,

Ali,

Masmoudi

et al. 2024

IET Systems Biology

View full text Add to dashboard Cite

Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid‐based deep learning model called Deep‐GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence‐based Trisection‐Position Specific Scoring Matrix (CST‐PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short‐term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST‐PSSM‐based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.

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.

IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design

Cited by 2 publications

References 56 publications

Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction

Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction

Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy

Contact Info

Product

Resources

About