ArticleCurrent computer-aided drug design2024
Identify Diabetes-related Targets based on ForgeNet_GPC.
Article in Current computer-aided drug design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundResearch on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.
aimsPolygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.
objectivesIn this study, a disease target identification algorithm based on protein recognition is proposed. MATERIALS AND
methodsIn 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.
resultsA novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.
conclusionIn 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.
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