Identify Diabetes-related Targets based on ForgeNet_GPC

Bin Yang; Linlin Wang; Wenzheng Bao

doi:10.2174/0115734099258183230929173855

Identify Diabetes-related Targets based on ForgeNet_GPC

Curr Comput Aided Drug Des. 2024 Jan 3. doi: 10.2174/0115734099258183230929173855. Online ahead of print.

Authors

Bin Yang¹, Linlin Wang¹, Wenzheng Bao²

Affiliations

¹ School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.
² School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, 221018, China.

PMID: 38173214
DOI: 10.2174/0115734099258183230929173855

Abstract

Background: Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.

Aims: Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.

Objective: In this study, a disease target identification algorithm based on protein recognition is proposed.

Methods: In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.

Results: A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.

Conclusion: In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

Keywords: protein; target; feature extraction; classification; diabetes.